EXCHANGE RATE VOLATILITY AND TRADE/PRODUCTIVITY IN AUSTRALIA

Transcription

1 The Universiy of Queensland Faculy of Business, Economics and Law School of Economics EXCHANGE RATE VOLATILITY AND TRADE/PRODUCTIVITY IN AUSTRALIA Approximae Word Lengh: 25,000 An Honours Thesis submied o he School of Economics, The Universiy of Queensland, in parial fulfilmen of he requiremens for he degree of BEcon (Honours). By Shuwei Jasmine Zheng 2 November 2005

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3 DECLARATION I declare ha he work presened in his Honours hesis is, o he bes of my knowledge and belief, original and my own work, excep as acknowledged in he ex, and ha maerial has no been submied, eiher in whole or in par, for a degree a his or any oher universiy. Shuwei Jasmine Zheng 2 November 2005 i

4 ACKNOWLEDGEMENTS Firsly, I would like o hank my supervisor Dr Philip Bodman for his assisance in he preparaion of his hesis and grea advice given hrough he year. More imporanly, I am hankful for his opimism and encouragemens ha have in many ways kep his year going. Secondly, I would also like o hank Dr Renuka Mahadevan for personal advices given hrough he year which I really do appreciae i a lo. Thirdly, hank you o my parens for heir invesmen in my educaion which has no been easy a imes. A big hank you also goes ou o all my friends who undersood and encouraged me hrough he roller-coasers of he year. And, o he Honours Class of 2005: Thank you for jus being here. Semeser 1 was grea fun albei he counless assignmens. Finally, THANK YOU o everyone who believes in me. ii

5 ABSTRACT In Ausralia, exchange rae volailiy has significanly increased afer he floaing of he exchange rae in On an inernaional basis, here have been concerns abou he possible derimenal effecs exchange rae volailiy could have on rade volumes and produciviy levels. Since produciviy growh and rade have been regarded as crucial facors in he promoion of susainable economic growh and improving he maerial living sandards for Ausralians, furher research ino he relaionship beween exchange rae volailiy and rade/produciviy will prove o be useful o policy makers. The objecive of his sudy is wofold. Firsly, in ligh of he Ausralia-Unied Saes (US) produciviy gap, his hesis will invesigae wheher here is reverse causaliy flowing from he real exchange rae movemens/volailiy o labour produciviy in Ausralia. The imporance of significan exchange rae depreciaion/volailiy having a negaive impac on produciviy has been highlighed by Harris (2002) for he case of Canada. Secondly, he impac of exchange rae volailiy on Ausralian expor volumes o US and Japan will be analysed. Alhough exensive heoreical and empirical research on his relaionship has been conduced, his issue has remained highly ambiguous. To dae, no empirical sudy has aken ino consideraion he hreshold effecs of exchange rae volailiy on expor volumes. Therefore, his hesis examines he relaionship beween exchange rae volailiy and rade/produciviy via linear and non linear hreshold esimaions of he rade and produciviy models. The non linear hreshold models esimaion in his hesis is based on Hansen (2000) s mehodology. To ensure ha sound economic inerpreaions of he models can be made, coinegraion ess will be conduced and Vecor Error Correcion Models (VECMs) will be esimaed. The economeric resuls obained from his hesis indicae ha long run equilibrium relaionships beween exchange rae volailiy and rade/produciviy exis. However, Granger Causaliy ess employed could no find evidences supporing he reverse causaliy link for he case of produciviy. Finally, significan hreshold effecs of exchange rae volailiy on rade/produciviy have been esed for, suggesing ha sandard rade/produciviy models involving exchange rae volailiy have undersaed heir rue underlying relaionships. iii

6 Keywords: Trade volumes, produciviy, exchange rae volailiy, reverse causaliy, non lineariy, hreshold effecs, Ausralia, asymmery iv

7 CONTENTS ABSTRACT... iii CHAPTER 1: INTRODUCTION Objecive Background Trade Produciviy Conribuions Brief Summary of Findings Srucure of hesis...10 CHAPTER 2: LITERATURE REVIEW TRADE Inroducion Basic Trade Uncerainy Models Exensions o he Basic Trade Uncerainy Models Hedging Opions Degree of Risk-Aversion and Profi Opporuniies Oher facors and frameworks Trade hyseresis Empirical Evidence Concluding Remarks...23 CHAPTER 3: LITERATURE REVIEW PRODUCTIVITY An Overview The Exchange Rae and Endogenous Produciviy Reverse Causaliy? Relaive Facor Cos Hypohesis Innovaion Gap Hypohesis Exchange Rae Shelering Hypohesis Asymmeric Produciviy Dynamics Model Oher Deerminans of Produciviy Empirical Evidence...36 v

8 3.6 Concluding Remarks...39 CHAPTER 4: DATA DESCRIPTION Inroducion Definiions, measuremen and sources of Variables Variables Included in he Trade Models Variables Included in he Produciviy Models...46 CHAPTER 5: MODEL SPECIFICATION AND METHODOLOGY Inroducion Model Specificaion The Basic Trade Model The Trade Threshold Model Esimaion of he Unknown Threshold Poin The Basic Produciviy Model The Produciviy Threshold Model Daa Pre-esing Uni Roo Tess Real Exchange Rae Volailiy Measures Mulivariae Coinegraion Analysis Johansen Coinegraion Tes Specificaion of number of lags Conducing he Johansen Coinegraion Tes VECM Esimaion Granger Causaliy Tess Innovaion Accouning Impulse Response funcions Variance Decomposiions...72 CHAPTER 6: EMPIRICAL ANALYSIS TRADE Inroducion Measuremen of Real Exchange Rae Volailiy Tess for Saionariy of Daa Economic Inerpreaion of he Basic Trade Models...84 vi

9 6.5 Esimaion of he Unknown Threshold Poin ( τ ) Economic Inerpreaion of he Trade Threshold Models Tess for Coinegraion VECM Esimaion Granger Non Causaliy Tess Innovaion Accouning Concluding Remarks CHAPTER 7: EMPIRICAL ANALYSIS PRODUCTIVITY Inroducion Measuremen of USD/AUD Real Exchange Rae volailiy Tess for Saionariy of Daa Economic Inerpreaion of he Basic Labour Produciviy Model Esimaion of he Unknown Threshold Poin ( ) τ Economic Inerpreaion of he Produciviy Threshold Model Tess for Coinegraion VECM Esimaion Granger Non Causaliy Tess Innovaion Accouning Concluding Remarks CHAPTER 8: CONCLUSIONS AND DIRECTIONS FOR FUITURE RESEARCH Summary Policy Implicaions Limiaions and Recommendaions for Fuure Research CHAPTER 9: DATA APPENDICES vii

10 LIST OF ABBREVIATIONS AND ACRONYMNS ABBR. ADF ARCH ARMA AUD B-S CPI FOP GARCH GARCH-M GDP ICT ITT IFS IRF KPSS OECD PP R & D US USD VECM YEN DESCRIPTION Augmened Dickey Fuller Auoregressive Condiional Heeroskedasiciy Auocorrelaion Moving Average Auocorrelaion Ausralian Dollar Balassa Samuelson Consumer Price Index Facors of Producion Generalised Auoregressive Condiional Heeroskedasiciy GARCH-in-Mean Model Gross Domesic Produc Informaion and Communicaions Technology Informaion Technology and Telecommunicaions Inernaional Financial Saisics Impulse response Funcion Kwiakowski, Phillips, Schmid, and Shin Organisaion for Economic Co-operaion and Developmen Phillips-Perron Research and Developmen Unied Saes Unied Saes Dollar Vecor Error Correcion Model Japanese Dollar viii

11 LIST OF FIGURES Figure 1.1: USD/AUD Real Exchange Rae Volailiy and Aus US Trade Volumes...4 Figure 1.2: YEN/AUD Real Exchange Rae Volailiy and Aus Japan Trade...5 Volumes...5 Figure 1.3: Per Capia GDP Growh OECD, US, Ausralia...6 Figure 1.4: Relaive Labour Produciviy: Ausralia vs. US...7 Figure 3.1: The Relaionship beween he Real Exchange Rae, Produciviy and Sandard of Living...26 Figure 3.2: Asymmeric Adverse Demand Shock...34 Figure 6.1: Time plo for LRUS...74 Figure 6.2: Correlogram for LRUS...75 Figure 6.3: Time plo for LRYEN...75 Figure 6.4: Correlogram for LRYEN...76 Figure 6.5 Impulse Response Funcions - Basic US Trade VECM...99 Figure 6.6: Impulse Response Funcions Basic Japan Trade VECM Figure 7.1: Time plo of LR Figure 7.2: Correlogram for LR Figure 7.3: Impulse Response Funcions Basic Produciviy VECM ix

12 LIST OF TABLES Table 2.1: Daa Sources for US/Japan Trade Models...45 Table 2.2: Daa Sources for Produciviy Models...48 Table 5.1: Deerminisic componens considered in he VECM...69 Table 6.1: Analysis of mean funcions for USD/AUD Real Exchange rae (LRUS)...77 Table 6.2: Analysis of mean funcions for YEN/AUD Real Exchange rae (LRYEN)...77 Table 6.3: USD/AUD Volailiy Analysis...79 Table 6.4: YEN/AUD Volailiy Analysis...81 Table 6.5: Summary Resuls of ADF, PP and KPSS ess (US)...82 Table 6.6: Summary Resuls of ADF, PP and KPSS ess (Japan)...83 Table 6.7: US and Japan Basic Trade Models...84 Table 6.8: Esimaed Threshold Values...86 Table 6.9: Threshold Trade Models...88 Table 6.10: Coinegraion Tes for US Basic Trade Model...92 Table 6.11: Coinegraion Tes for Japan Basic Trade Model...93 Table 6.12: Error Correcion Terms from Basic US Trade VECM...95 Table 6.13: Error Correcion Terms from Basic Japan Trade VECM...95 Table 6.14: Granger Causaliy Resuls based on Basic US Trade VECM...97 Table 6.15: Granger Causaliy Resuls based on Basic Japan Trade VECM...97 Table 6.16: IRFs for Basic US Trade VECM...98 Table 6.18: Variance Decomposiion for US Table 7.1: Analysis of mean funcions for USD/AUD Real Exchange rae (LR) Table 7.2: USD/AUD Volailiy Analysis Table 7.3: Resuls of ADF, PP and KPSS ess Table 7.4: Resuls for he Basic Labour Produciviy Model Table 7.5: Resuls for he Esimaed Threshold Value Table 7.6: Resuls for he Labour Produciviy Threshold Model Table 7.7: Johansen Coinegraion Tes for he Basic Produciviy Model Table 7.8: Error Correcion Terms from Basic Produciviy VECM x

13 Table 7.9: Granger Causaliy Resuls based on Basic Produciviy VECM xi

14 CHAPTER 1: INTRODUCTION Our sandard of living he qualiy of our communiies, he prosperiy of our families, and he securiy of our jobs depends more han ever on our abiliy o compee in he global markeplace - Mark Vaile, Ausralian Miniser for Trade (April 2002) Produciviy isn' everyhing, bu in he long run i is almos everyhing. A counry's abiliy o improve is sandard of living over ime depends almos enirely on is abiliy o raise is oupu per worker. - Paul Krugman (1992, p. 9) Over long periods of ime, small differences in raes of produciviy growh compound, like ineres in a bank accoun, and can make an enormous difference o a sociey's prosperiy. Nohing conribues more o reducion of povery, o increases in leisure, and o he counry's abiliy o finance educaion, public healh, environmen and he ars. - Alan Blinder and William Baumol (1993, p. 778) 1.1 Objecive I is indeed rue ha produciviy and rade performances are of serious concern and opical relevance. For a small, ye open economy like Ausralia, produciviy growh and rade performance are known o be key moivaing facors behind he promoion of susainable economic growh and he improvemen of maerial living sandards. Since he floaing of he Ausralian exchange rae in December 1983, here has been an observed increase in exchange rae volailiy. This has also been he general phenomenon observed from an inernaional perspecive. This increase in exchange rae volailiy led Harris (2001) o argue for he case of Canada ha exchange rae depreciaion as well as he increase in volailiy could have a significan negaive impac on produciviy here. Since 1

15 he exchange rae floa in 1983, here have also been concerns abou he possible derimenal effecs exchange rae volailiy could have on rade volumes. While here have been several previous empirical sudies on his issue, no consensus have been reached. One noable feaure of hese previous sudies has been he implici assumpion of symmery in he effec of exchange rae volailiy on rade volumes. These observaions led o he following quesions his hesis seeks o address in he conex of Ausralia: 1. Has susained exchange rae depreciaions, observed paricularly in he 1990s, as well as he increase in exchange rae volailiy (pos-floa) led o a negaive impac on labour produciviy? 2. Has he increase in exchange rae volailiy since he floa negaively impaced expor volumes in Ausralia? 3. Do hreshold effecs of exchange rae volailiy on labour produciviy and expor volumes exis? If so, can his provide a beer undersanding of he rue underlying relaionship beween exchange rae volailiy and produciviy/rade? The res of his chaper will be organised as follows. Secion 1.2 will review he background of produciviy and rade performances in he conex of Ausralia providing a moivaion for his hesis. The conribuions of his hesis o he exising lieraure will be discussed in Secion 1.3. In Secion 1.4, he srucure of he hesis will be saed. 1.2 Background There are valid reasons as o why Ausralians should be concerned abou he produciviy and rade performances of Ausralia. The following wo sub-secions will succincly explain he imporance of produciviy and rade performances o he improvemen of sandard of living for Ausralians. 2

16 1.2.1 Trade One imporan facor conribuing o an improvemen of he maerial sandard of living for Ausralians is rading relaions beween Ausralia and he res of he world. More specifically, his hesis is concerned wih Ausralian expor volumes. According o DFAT (2005b), 1 in 5 Ausralian jobs relies on expors. As such, a 10 per cen increase in expors could creae 70,000 new Ausralian jobs. Wih he move owards floaing exchange raes, here have been concerns abou he possible derimenal effecs exchange rae volailiy could have on rade volumes boh on an inernaional basis and in he conex of Ausralia. However, based on he exising heoreical models and previous empirical sudies conduced in his area, here has been no consensus reached on he impac of exchange rae volailiy on rade volumes. Depending on he assumpions made, he impac could be posiive, negaive or even zero. According o DFAT (2005c), he wo major expor markes for Ausralia are he Unied Saes (US) and Japan. Hence, i may be useful o look a he movemen of rade volumes and exchange rae volailiy for each marke over ime. Figures 1.1 and 1.2 show he relevan movemens in rade volumes and real exchange rae volailiy for he US and Japan respecively. For Figures 1.1 and 1.2, rade volumes are calculaed by summing he real expor volumes o US/ Japan and real impor volumes from US/Japan. The real exchange rae is expressed in volume noaion forma. This implies ha an appreciaion of he exchange rae is equivalen o an increase in he exchange rae. The real exchange rae volailiies are modelled using he Generalised Auoregressive Condiional Heeroskedasic (GARCH) procedure. 3

17 Figure 1.1: USD/AUD Real Exchange Rae Volailiy and Aus US Trade Volumes Millions Real US/AUD Volailiy Q2 1989Q2 1990Q2 1991Q2 1992Q2 1993Q2 1994Q2 1995Q2 1996Q2 1997Q2 1998Q2 1999Q2 2000Q2 2001Q2 2002Q2 2003Q2 2004Q2 Year Aus-US Trade Volume Real US/AUD Vol Source: Auhor s own calculaion of volailiy measure based on USD/AUD Real exchange rae obained from he RBA Bullein Daabase in dx Daabase; Expor and Impor Volumes obained from ABS Time Series Saisics Plus in dx Daabase As seen in Figure 1.1, in some of he years, here appears o be a negaive associaion beween he USD/AUD real exchange rae volailiy and he volume of rade. This can be observed from 1999:2 o 2000:2. Similarly for he case of Japan, as observed from Figure 1.2 overleaf, from 1995:2 o 1996:2, he exreme increase in YEN/AUD real exchange rae volailiy can be seen as having a negaive associaion wih rade volumes beween Ausralia and Japan. The general increase in rade volumes beween Ausralia and Japan from 1997 reflecs he increasing imporance of Japan as a rading parner o Ausralia in recen years. 4

18 Figure 1.2: YEN/AUD Real Exchange Rae Volailiy and Aus Japan Trade Volumes Millions Real YEN/AUD Volailiy Q2 1989Q2 1990Q2 1991Q2 1992Q2 1993Q2 1994Q2 1995Q2 1996Q2 1997Q2 1998Q2 1999Q2 2000Q2 2001Q2 2002Q2 2003Q2 2004Q2 Year Aus-Japan Trade Volumes Real Yen/AUD Vol Source: Auhor s own calculaion of volailiy measure based on YEN/AUD Real exchange rae obained from he RBA Bullein Daabase in dx Daabase; Expor and Impor Volumes obained from ABS Time Series Saisics Plus in dx Daabase Of course, he negaive associaion observed in boh Figures 1.1 and 1.2 may no reflec a causal relaionship. Hence, furher research needs o be underaken o empirically analyse he relaionship beween exchange rae volailiy and rade volumes for he case of he US and Japan. Moreover, one common assumpion made by earlier sudies in his area has been he symmeric effec of exchange rae volailiy on rade volumes. This is unrealisic as i can be shown ha i is possible for firms o reac differenly o varying degrees of volailiy (exchange rae risk). The explanaion for his is aken on furher in Chaper 3. 5

19 1.2.2 Produciviy Over he pas hree decades, wo-hirds of he improvemen in average real incomes of Ausralians has been aribuable o produciviy growh (Indusry Commission 1997). In paricular, by he end of he 1990s, Ausralian households were geing an addiional $7000 increase in income on average as compared o he 1980s (Produciviy Commission 2004). According o Figure 1.3 below, Ausralia s GDP per capia growh has increased by more han 0.5 per cen faser han he US and he OECD counries in he 1990s and 2000s. Figure 1.3: Per Capia GDP Growh OECD, US, Ausralia Per cen s 1960s 1970s 1980s 1990s 2000s OECD Unied Saes Ausralia Source: Groningen Growh and Developmen Cenre and The Conference Board, Toal Economy Daabase, Augus 2005, available a hp:// accessed 10 Sepember Using Gross Domesic Produc (GDP) per capia as an aggregae indicaion of living sandards, Ausralia s GDP per capia relaive o ha of he US improved from approximaely 76 per cen in 1983 o 79 per cen in However, his is only slighly above he relaive posiion ha Ausralia was in during he 1950s. While Ausralia s GDP per capia has clearly improved, i is sill below he bes in OECD including counries like he US, Swizerland and Luxembourg. 6

20 Clearly much of he increase in GDP per capia has been due o a cach up in Ausralia s relaive produciviy level. In Figure 1.4 below, i can be observed ha Ausralia s produciviy level relaive o US has risen from around 78 per cen o 85 per cen in As Blinder and Baumol (1993) argued, even small bu coninuous improvemens in produciviy add value o he economy. According o he Indusry Commission (1997), mainaining a 2 per cen produciviy growh per year could raise average real incomes of he nex generaion by an addiional 13 per cen and he generaion afer ha by an addiional 30 per cen. Figure 1.4: Relaive Labour Produciviy 1 : Ausralia vs. US Ausralia as per cen of US GDP per hour GDP per person Source: Daa for Ausralia-US Relaive Labour Produciviy obained from Groningen Growh and Developmen Cenre and he Conference Board, Toal Economy Daabase, accessed 10 Ocober 2005, available a hp:// 1 Two commonly used measures of labour produciviy are shown in Figure 1.2. This hesis defines real labour produciviy as real indusry value added oupu divided by oal hours worked in he economy. While he per person measure appears o undersae real labour produciviy, similar rends can be observed in boh measures. 7

21 While Figure 1.4 indicaes ha Ausralia s produciviy growh has hi a record high in he 1990s, he economy appears o remain in a cach-up phase from an inernaional perspecive. Moreover, in recen years following he peak in 1998, Ausralia s produciviy level relaive o US has in fac been declining, o approximaely 80 per cen in As Ausralia is a relaively small open economy, he produciviy levels achieved for he US aggregae economy may no be feasible wih is curren se of resource endowmens. Noneheless, he produciviy gap beween Ausralia and he US and some oher OECD counries remain indicaive of he fac ha Ausralia s poenial o achieve even higher produciviy levels has no been diminished. Wih he recen decline in Ausralia s relaive produciviy levels o he US from approximaely 85 per cen in 1998 o 80 per cen in 2004, he issue of a poenially widening produciviy gap beween Ausralia and US becomes increasingly imporan and warrans furher research. Indeed, in recen years, he Produciviy Commission s key area of research has included he monioring and invesigaing of deerminans of produciviy growh. Much of hese effors have mainly concenraed on microeconomic policy reforms and heir impac on produciviy improvemens in he economy. As a resul, a range of indicaors confirm ha srucural reforms, including naional compeiion policy, have been he principal conribuors o ha surge in produciviy and income (Produciviy Commission 2004). To ensure coninuous fuure improvemens o Ausralians sandard of living, produciviy improvemens are clearly essenial. While hese earlier srucural reforms have been successful, i has o be noed ha his does no guaranees fuure success. Hence, i appears imperaive o examine oher possible deerminans of produciviy levels in Ausralia. As menioned earlier, Harris (2002) has suggesed he possibiliy of exchange rae facors negaively influencing produciviy levels for he case of Canada. The hree main reasons ha have been suggesed for his causal channel and discussed in Chaper 3 includes: he relaive facor cos hypohesis, he innovaion gap hypohesis and he shelering exchange rae mechanism. 8

22 In ligh of he preceding discussions, he research in his hesis seeks o empirically analyse and esablish a more in-deph undersanding on an aggregae basis of he relaionship beween real exchange rae depreciaions/volailiy and labour produciviy levels in Ausralia. In addiion, an empirical invesigaion of he relaionship beween real exchange rae volailiy and Ausralian expor volumes o he US and Japan is also underaken. The empirical analyses will be conduced via a sandard linear approach, as well as he nonlinear modelling approach oulined in Hansen (2000) o uncover possible asymmeries and hreshold effecs. The shor run dynamics of he relaionships analysed will also be invesigaed in his hesis. 1.3 Conribuions In achieving hese objecives, his hesis conribues o he exising lieraure in he following manner: 1. Provides a formal esimaion of he relaionship beween real exchange rae depreciaions/volailiy and real labour produciviy in he conex of Ausralia. 2. Incorporaes he possibiliy of asymmeries and hreshold effecs of exchange rae volailiy on labour produciviy in Ausralia. 3. Esablishes he relaionship beween real exchange rae volailiy and Ausralian expor volumes o he US and Japan and mos imporanly, his hesis will be he firs o empirically esimae he hreshold effec of exchange rae volailiy on expor volumes o he US and Japan. 1.4 Brief Summary of Findings The main findings of his hesis from he empirical analyses conduced can be summarised as follows: 1. No evidence is found o subsaniae he claim ha real exchange rae depreciaions negaively influence labour produciviy levels in Ausralia. In fac, empirical evidence found in his sudy suppored he compeiiveness approach which saes ha exchange 9

23 rae depreciaions will increase inernaional price compeiiveness, hus increasing produciviy and hence oupu growh. 2. However, he inclusion of he hreshold effec of USD/AUD real exchange rae volailiy appears o affec he real labour produciviy levels in Ausralia in a significan negaive manner. 3. The evidence appears o subsaniae he Balassa-Samuelson hypohesis, bu no he reverse causaliy link as suggesed by Harris (2002). 4. The basic rade models show ha he impac of real exchange rae volailiy on Ausralian real expor volumes o he US and Japan are negaive and posiive respecively. However, when he hreshold effec of real exchange rae volailiy is accouned for, here is an unambiguous significan overall negaive impac on real expor volumes o he US and Japan. 1.5 Srucure of hesis The ouline of he hesis is as follows. The nex chaper will review he lieraure surrounding he effec of exchange rae volailiy on rade volumes. Specifically, he reasons behind he empirical ambiguiies observed in previous sudies will be discussed, relaing hem back o rade hyseresis. A review of he previous empirical sudies in his area will also be provided, wih is limiaions highlighed. Chaper 3 provides he lieraure review on he relaionship beween exchange rae depreciaions/volailiy and labour produciviy. In paricular, reasons behind he proposed reverse causaliy link will be looked a. A review of he exising, limied empirical sudies will also be provided. Chaper 4 provides a succinc descripion of he definiions, measuremen and daa sources of he variables used in his sudy. This is followed by a deailed discussion in Chaper 5 of he various empirical mehodologies employed in his sudy, including derivaion of he volailiy measures, uni roo ess, esimaion of he hreshold models and esimaion of vecor error correcion models (VECMs). Chaper 6 and 7 presens he economeric resuls from he analysis on expor volumes and labour produciviy respecively. Finally, Chaper 8 offers some concluding remarks, as well as he implicaions of his sudy for policy 10

24 formulaion, limiaions of he sudy and recommendaions for fuure research. A complee daa appendix is provided in Chaper 9. 11

25 CHAPTER 2: LITERATURE REVIEW TRADE 2.1 Inroducion Afer he breakdown of he Breon Woods sysem, fixed exchange rae sysems in mos counries were replaced by some form of flexible sysem of exchange rae deerminaion. In Ausralia and many oher counries, an observable difference beween he fixed and floaing exchange rae regimes has been he significan increase in exchange rae volailiy ha has accompanied he swich o flexible raes. Whils here have been many argumens in favour of flexible exchange raes, here have also been concerns raised by some commenaors, including he poenial derimenal effecs exchange rae volailiy can have on rade volumes (and produciviy levels). However, oher auhors argue ha exchange rae volailiy has eiher no effec or even possibly a posiive effec on rade volumes. Depending on he assumpions made, differen heoreical models arrive a opposing conclusions. Empirical sudies in he recen lieraure have also been ambiguous. Perhaps, he only consensus reached on his issue has been ha he impac of exchange rae volailiy on rade volumes, if any, is difficul o esimae. This chaper will be organised as follows. In Secion 2.2, he earlier basic rade uncerainy models, which mainly argue for he negaive impac of exchange rae volailiy on rade volumes, are discussed. In Secion 2.3, discussions on he various exensions made o he basic rade uncerainy models are presened. These exensions include considering he degree of risk-aversion, he plausibiliy of hedging and looking a his issue from various oher frameworks. An overview of he exising lieraure on rade hyseresis is presened in Secion 2.4. This is followed by a review of he recen empirical sudies on he relaionship beween exchange rae volailiy and rade volumes oulined in Secion 2.5. Finally, concluding remarks are provided in Secion

26 2.2 Basic Trade Uncerainy Models One of he earlier heoreical sudies ha invesigaed he impac of exchange rae volailiy on rade volumes was Clark (1973). The auhor developed a model ha consiss of a perfecly compeiive exporing firm wih no marke power. The firm produces one homogenous commodiy and uses no impored inpus. I is assumed ha he firm is paid in foreign currency whereby expors revenues are convered a he curren exchange rae. Hedging is limied. In addiion, oupu is consan over he planning horizon. Due o he coss involved in adjusing producion scales, he firm mus make producion decisions before observing any exchange rae movemens. In making producion decisions, uncerainy abou fuure exchange raes, which ranslaes ino uncerainy on revenues from fuure expor receips in domesic currency, mus be accouned for. Consequenly, he exporing firm is unable o aler is oupu in response o volailiy in he real exchange rae, wheher he movemen in he real exchange rae is favourable or unfavourable. In his insance, he uncerainy on he firm s expor revenues is enirely dependen on exchange rae risk. Therefore, higher volailiy in exchange raes resuls in uncerainy abou expeced profis from rading. This in urn causes hese firms o reduce ransacions. Oher early heoreical sudies ha had made similar assumpions and argumens include Hooper and Kohlhagen (1978) and Ehier (1973). An opposing viewpoin was developed in Baron (1976). He produced a model ha shows ha an increase in exchange rae volailiy may no necessarily influence he level of rade. The model relaxes he assumpion of perfec compeiion and focuses on he role of invoicing currency. In his insance, he exporing firm faces price risk if conracs are invoiced in he foreign currency. 2 While he quaniy demanded is known as prices do no change during he conrac period, he revenue sream and profis are uncerain. However, if he foreign currency is depreciaing, he loss o he exporer is parly offse by he higher foreign currency expor price, a poin furher developed in Cushman (1983). In addiion, Clark (1973) also noed ha producion coss could be lowered if he exporing firms were 2 I should be noed ha his is applicable o Ausralia as according o ABS (2005), he US dollar accouned for 69 per cen of expor invoices in 2004, and 72 per cen in March quarer

27 o use impored inpus from a counry whose currency is depreciaing, which would offse he declining expor revenues also. 2.3 Exensions o he Basic Trade Uncerainy Models These earlier rade uncerainy models gave us an idea of how exchange rae volailiy could possibly have a negaive impac on rade volumes. However, hese models were dependen on resricions ha are considered oo rigid for an indusrialised counry like Ausralia. The following sub-secions will involve relaxing hese assumpions and analysing he resuling impac on rade volumes Hedging Opions Broll (1994) considers he possibiliy of hedging by using forward conracs and opions in he presence of a forward exchange marke. Furhermore, i is assumed ha capial invesmens are usually made before producion begins and he firm finances is capial invesmens using is own resources and by borrowing in he capial marke. Under hese assumpions, he auhor analyses he economic behaviour of a risk-averse firm producing in a foreign counry facing random exchange raes. I is found ha exchange rae volailiy only affecs he level of hedging. The level of foreign producion and capial allocaion only depends on he forward rae. In his case, he heoreical model finds ha exchange rae volailiy does no influence rade volumes. In oher sudies, Ehier (1973) and Baron (1976) also concluded ha wih perfec forward markes and he only source of uncerainy coming from he exchange rae, he volume of rade is unlikely o be affeced by exchange rae volailiy. On he oher hand, Viaene and de Vries (1992) argued ha i is possible for rade volumes o be indirecly affeced by he spo exchange rae hrough is effec on he forward rae even when here is a forward marke. The auhors posulae ha exporers and imporers are on opposing sides of he forward marke resuling in opposie effecs on expors and 14

28 impors. Hence, in his case, expors benefi (lose) and impors lose (benefi) when he rade balance is negaive (posiive) or when he forward risk premium is negaive (posiive). However, while he exisence of forward markes in indusrialised economies is argued o reduce he effec of exchange rae volailiy on rade volumes, here are also reasons why firms may choose no o eliminae he exchange rae risk hrough forward conracs. Firsly, forward markes may be non-exisen in developing counries. On he oher hand, for indusrial counries like Ausralia, managing relevan fuures porfolios may be cosly even hough shor erm exchange rae risk can be easily hedged. In addiion, hedging he exchange rae over he medium o long erm is argued o be more difficul as forward conracs are ypically for he shor erm (Coe 1994). Specifically, i has been argued ha hedging may be more difficul and cosly for a manufacuring firm ha eners ino longer erm sales conracs. This is applicable in he Ausralian conex as manufacured goods expors have been growing seadily, wih an increase by 6.5 per cen o $35.1 billion in (DFAT 2005a) Degree of Risk-Aversion and Profi Opporuniies The basic rade models discussed in Secion 2.2 showed ha under he assumpion ha firms are risk-averse, rade volumes will decline when exchange rae volailiy increase. However, several heoreical sudies have showed ha i is possible for rade volumes o increase even when here is an increase in risk. Using a model consising of a perfecly compeiive firm ha is able o spread is allocaion beween domesic and foreign markes, De Grauwe (1988) shows ha he degree of firm s risk-aversion will affec he impac exchange rae volailiy has on rade volumes. I is posulaed ha when firms are relaively risk-averse, an increase in risk will increase he expeced uiliy from expor revenue and hence encourages expors volume o increase. On he oher hand, exremely risk-averse firms may expor even more when exchange rae volailiy increases in order o avoid an exreme decline in expor revenues. 15

29 In a more recen paper, De Grauwe (1992) considers a simple model consising of a priceaking firm wihou adjusmen coss. When exchange rae volailiy is greaer, he higher price will encourage he firm o increase oupu producion in order o aain higher revenues. However, his is dependen on he firm s degree of risk aversion. The higher variance of profis has a negaive impac on he firm and hence discourages he firm from exporing. However, if he firm is relaively no risk-averse, he posiive effec from he greaer profis derived from greaer exchange rae variabiliy ouweighs he negaive impac from he higher variance of profis. In his insance, he firm will increase producion of oupu and hus expor more. Alernaively, Gros (1987) considers a more realisic model wih adjusmen coss. The model is based on a compeiive firm, whereby all oupu produced is expored. Risk neuraliy is also assumed. The applicaion of his model shows ha a firm s oupu can be increased when here is an increase in exchange rae volailiy, if here are some facors of producion (FOP) ha are flexible. In his sense, producion is considered as an opion. Similar o he previous model, producion can be increased when he price is high by using more flexible FOP, such ha profis increase more han proporionaely, vice versa. The firm is consrained in is use of his opion, depending on he capaciy of he firm o increase he use of flexible FOP. An earlier sudy by Pindyck (1982) also uilised a similar approach and arrived a he same conclusion Oher facors and frameworks There has been criicism ha i is unrealisic o assume exchange rae volailiy as he only source of uncerainy. I is possible ha exchange rae movemens could affec oher facors which indirecly influence rade volumes. Hence, i has been argued ha hese oher facors should be aken ino accoun when examining he link beween exchange rae volailiy and rade volumes. Cushman (1986) argues ha he analysis of he impac of exchange rae volailiy on aggregae and bilaeral rade flows should include movemens in oher exchange raes or 16

30 ineres raes. The heoreical model derived by he auhor argues ha rade will be shifed away from he counry where exchange rae volailiy has increased o anoher counry where volailiy is lower if he exporing firm is able o expor o differen counries. Consequenly, bilaeral rade flow esimaions ha omi oher exchange raes volailiy are biased owards finding a posiive relaionship. As seen in he model derived by Cushman (1986), oher facors such as anoher counry s exchange rae volailiy may have an influence on he level of rade. However, hese addiional facors have been assumed o be unchanged in he parial equilibrium framework which he preceding models have adoped. Hence, auhors such as Clark e al. (2004) have argued ha a general equilibrium framework should be adoped o ake accoun of he possible ineracion beween all variables in order o obain he rue underlying relaionship beween exchange rae volailiy and rade volumes. A recen sudy by Bacchea and van Wincoop (2000) adoped a wo counry general equilibrium framework whereby volailiy is caused by moneary, fiscal and echnology shocks. The auhors compare he rade volumes and welfare under he fixed and floaing exchange rae regimes. The sudy finds no clear relaionship beween he level of rade and he exchange rae regime, he same ambiguiy parial equilibrium approaches described. In paricular, a moneary expansion in an economy would cause he exchange rae o depreciae leading o a reducion in impors. However, he increased demand due o he moneary expansion could possibly offse his effec. 2.4 Trade hyseresis In recen years, rade hyseresis has become increasingly imporan as previous heoreical and empirical sudies on he relaionship beween exchange rae volailiy and rade volumes failed o yield conclusive resuls. A saring poin for discussion is o consider he role of sunk coss. Inernaional rade beween counries usually involves differeniaed manufacured goods (which has become 17

31 increasingly imporan in Ausralia, growing by 6.5 per cen in (DFAT 2005a)). This requires significan capial invesmen on he firms par which includes seing up producion faciliies ha caer o he expor markes. Due o he sunk coss involved, firms are usually less responsive o exchange rae volailiy. Hence, firms can be seen as adoping a wai-and-see approach, wih he opion of exiing he marke (Clark, P. e al. 2004). The opion framework has also been adoped by Dixi (1989) who suggess ha higher exchange rae volailiy implies greaer reurns in fuure. Hence, firms are likely o adop a wai-and-see approach, delaying enry and exi decisions. In urn, his means ha he indusry is less responsive o exchange rae movemens as he hyseresis bands widen. In anoher sudy, Franke (1991) uses an ineremporal, infinie horizon framework o analyse he effecs of exchange rae volailiy on he expor sraegy of a firm. In his model, here are ransacion coss. Enry coss are incurred when he firm sars exporing. The firm is assumed o be operaing under a monopolisic compeiion framework and is riskneural. Franke (1991) models volailiy using he real exchange rae, assuming i follows a mean-revering process. In his case, he firm assess is enry (exi) coss agains he profis (losses) from expors. On average, he firm eners sooner and exis laer when exchange rae increases sufficienly, increasing he number of firms in he marke. In his insance, here is a hreshold effec of exchange rae volailiy. In addiion, his model posulaes ha exchange rae volailiy acually increases rade, which in urn, sees firms exiing laer. The auhor argues ha rade volumes are likely o increase when exchange rae volailiy increase due o he imperfecion in he goods marke. Arbirage opporuniies for inernaional rade are creaed when he law of one price is violaed. However, he wo assumpions of risk neuraliy and mean-revering exchange raes made by Franke (1991) are oo resricive. The model derived by Sercu and Vanhulle (1992) modifies hese resricions and insead assume risk aversion, perfec hedging and a random process for he real exchange rae. The auhors also based heir model on sunk coss (as discussed earlier on) insead of enry/exi coss o analyse he behaviour of an exporing firm. I is posulaed ha when he exchange rae drops below a cerain hreshold, he firm 18

32 has o make a decision beween exiing he marke, and sopping rade emporarily. If he former is underaken, all expendiures are sopped. Re-enry is no possible. If he laer is underaken insead, he firm coninues o incur fixed coss while flexible coss cease. Hence, as seen from his model, i is likely ha exi decisions can be delayed despie an increase in exchange rae volailiy. Therefore, as discussed in his secion, i is possible ha in order for he rue underlying relaionship beween exchange rae volailiy and rade volumes o be analysed, he hreshold effec of exchange rae volailiy has o be accouned for. For he various facors discussed above, here are reasons o believe ha depending on he degree of exchange rae volailiy, he impac on expor volumes will vary. When exchange rae volailiy increases, he exporing firm has o weigh he coss associaed wih adjusing he rade volume agains he benefis from adjusmen. Hence, if he associaed coss exceed he expec profis, here is ineria in firms decision o adjus he rade volumes. In Chaper 6, his hreshold effec of exchange rae volailiy is empirically analysed. 2.5 Empirical Evidence The empirical evidence on he relaionship beween exchange rae volailiy and rade has also been mixed. This secion reviews some of he recen empirical work in he lieraure. A survey of he early empirical work done on he relaionship beween exchange rae volailiy and rade volumes was presened in IMF (1984). The sudy did no sugges consisen resuls, wih many of he sudies discussed finding lile or no suppor for a negaive effec. There are several reasons ha migh have conribued o he lack of robus findings in hese earlier sudies, including he fac ha here were no conclusive findings amongs he heoreical sudies and ha he observaions included ofen capured only a relaively shor period where exchange raes showed significan variaion. One of he many empirical sudies ha was done afer he IMF (1984) sudy was ha of Chowdhury (1993). This paper invesigaes he impac of exchange rae volailiy on he 19

33 rade flows of he G-7 3 counries using a mulivariae error-correcion model framework. The model was esimaed for each of he G-7 counries over he sample period The auhor used a moving sample sandard deviaion of he growh rae of he real exchange rae as a proxy for real exchange rae volailiy. Assuming ha marke paricipans are risk-averse, he sudy finds ha exchange rae volailiy has a significan negaive influence on he expors volumes in each of he G-7 counries. More imporanly, he paper argues ha previous sudies have failed o ake ino accoun he fac ha mos of he macroeconomic variables included in rade equaions are non-saionary. Hence, he weak relaionships found in previous sudies may be aribuable o his reason. Similar resuls conduced on oher economies were obained by Arize (1996). The auhor also uses a mulivariae error-correcion model o examine he impac of real exchange rae volailiy on he rade flows of eigh European economies. 4 The resuls sugges ha here is a significan negaive long run relaionship beween real expors and exchange rae volailiy in each of he counry invesigaed. This is regardless of wheher hey are par of he European Exchange-rae Mechanism (ERM). Similarly, Sukar and Hassan (2001) finds a significan and negaive relaionship beween US expors and exchange rae volailiy. However, he effecs of boh real exchange rae and exchange rae volailiy are insignifican in he shor run. In conras, posiive effecs of exchange rae volailiy have also been obained in several sudies. Asseery and Peel (1991) examines he influence of real exchange rae volailiy on aggregae expor volumes. In his case, he auhors used quarerly daa from , for 5 counries including Ausralia, Japan, UK, US and (Wes) Germany. The proxy for he volailiy variable in he paper was he squared residual from he ARIMA process fied o he real exchange. The auhors provided evidence of a significan and posiive effec of real exchange rae volailiy on all counries excep for UK. McKenzie and Brooks (1997) 3 The G-7 counries include: Canada, France, Germany, Ialy, Japan, Unied Kingdom (UK) and he US. 4 The eigh European economies include: Belgium, Denmark, Finland, France, Greece, Neherlands, Spain and Sweden. 20

34 analyses he impac of exchange rae volailiy on Germany-US bilaeral rade flows, using monhly daa spanning 1973:4 1992:9. The volailiy measure used in he sudy was generaed using an ARCH model. Similarly, he auhors find a significan posiive relaionship beween volailiy and Germany-US rade flows. In addiion, Daly (1998) uses daa from 1978:1 1992:2 for eigh counries. 5 The auhor uses models capuring bilaeral rade flows in which he null hypohesis of zero exchange rae volailiy effec is hen esed. Oher facors ha are likely o affec rade volumes such as economic aciviy, relaive prices and coss were also been included in he models. The major finding of he sudy was ha exchange rae volailiy is likely o lead o a posiive impac on rade volumes in Japan. Oher sudies like McKenzie (1998) repors mixed resuls. The auhor analyses he impac of exchange rae volailiy on Ausralian rade flows. The proxy for exchange rae volailiy is generaed using ARCH models in a framework consising of Ausralian impors and expors. More imporanly, he auhors esed for exchange rae volailiy effecs using aggregae and disaggregaed secoral rade daa. The resuls of he sudy sugges ha he impac of exchange rae volailiy on rade volumes differ beween raded good secors bu ha i remains difficul o esablish a clear relaionship. Specifically, on a disaggregaed basis, i is found ha exchange rae volailiy posiively influence Ausralian meal ore and minerals expor secor and negaively affec he Ausralian inermediae goods secor. In conras, he aggregae expor and impor daa provided limied evidence o sugges ha Ausralian expors are affeced posiively by exchange rae volailiy while impors are influenced negaively. However, he explanaory power of he equaions was relaively low and few variables were saisically significan. Arize and Malindreos (1998) conduced an empirical sudy on he relaionship beween expor volumes and exchange rae volailiy for Ausralia and New Zealand, using quarerly daa from The auhors uilised mulivariae coinegraion and errorcorrecion modelling procedures. Their analysis also incorporaed daa ha covers he 5 The eigh counries include: Ausralia, Canada, France, Germany, Japan, Ialy, UK and he US. 21

35 floaing exchange rae regime o allow for beer policy implicaions. The empirical resuls indicae ha here is a posiive and significan long run relaionship beween expor volumes and exchange rae volailiy in Ausralia, bu a negaive and significan long run relaionship for New Zealand. Klein (1990) also finds differen effecs across caegories of expors when using a fixed effec framework o es for he effecs of exchange rae volailiy on nine caegories of goods expored from he US o seven major indusrialised counries. A major poin o make is ha all he empirical sudies discussed above have assumed ha he effec of exchange rae volailiy on rade volumes is symmeric. This is poenially oo resricive. As discussed in he Secion 2.4, i is possible ha risk-averse exporers behave differenly over differen degrees of exchange rae volailiy. Few empirical sudies have direcly incorporaed he asymmeric effecs of exchange rae volailiy on rade volumes ino he esimaion of models. Moreover, he sudies ha have incorporaed asymmeries have focused on he asymmeric effecs of exchange rae volailiy from he perspecive of appreciaions and depreciaions. Specifically, Tse and Tsui (1997) invesigaes he condiional volailiy of he Malaysian US dollar and Singapore US dollar exchange rae. They find ha a depreciaion shock produces a greaer effec on fuure volailiy in exchange raes compared o a similar appreciaion shock. In addiion, Fang and Thompson (2004) find ha expors respond posiively o depreciaions and negaively o exchange rae volailiy, bu he overall effec only adds noise o expor fundamenals. Up o now, he only sudy ha has really looked a he issue of asymmeric effecs of exchange rae volailiy has been Fang, Lai and Miller (2005). The auhors invesigae if expors reac differenly o exchange rae volailiy in appreciaions and depreciaions. Using a bivariae GARCH-M model wih dynamic condiional correlaion, hey invesigae he exchange rae effec on expors. They also es for exisence of asymmery. Monhly daa on bilaeral expors from eigh Asian counries o he US spanning from

36 are used. 6 The resuls of he sudy indicae ha here is a posiive exchange rae depreciaion effec and ha real exchange rae volailiy produces significan and eiher negaive or posiive effecs in all counries examined. More imporanly, all eigh counries examined exhibi weak or srong asymmery wih respec o exchange rae volailiy during exchange rae appreciaions and depreciaions. Darby e al. (1999) used he concep of he hreshold effec o invesigae he impac of exchange rae volailiy on he level of invesmen under a Dixi-Pindyck Model framework. However, i seems clear ha he concep of he hreshold effec has no been adequaely applied in he examinaion of exchange rae volailiy impac on rade volumes. 2.6 Concluding Remarks This chaper has summarised he various differen heoreical models and empirical sudies conduced wih regard o he relaionship beween exchange rae volailiy and rade volumes. I has also been shown ha previous empirical sudies have assumed symmery in he effecs of exchange rae volailiy on rade volumes. As such, he exisence of a hreshold effec of exchange rae volailiy has been ignored and his could be one reason why previous empirical sudies have failed o come o any consensus on his issue. In ligh of he limiaions in previous empirical sudies, his hesis seeks o furher address his issue by incorporaing hreshold effecs ino he economeric framework o analyse he rue underlying relaionship beween real exchange rae volailiy and real Ausralian expor volumes o he US and Japan. In addiion, basic rade models for boh counries will also be esimaed o allow for comparison beween he wo differen frameworks. This empirical issue will be furher addressed in Chaper 6. 6 The eigh Asian counries include Indonesia, Japan, Korea, Malaysia, Philippines, Singapore, Taiwan and Thailand. 23

37 CHAPTER 3: LITERATURE REVIEW PRODUCTIVITY 3.1 An Overview The lieraure on he relaionship beween exchange rae movemens and produciviy dynamics has been disparae. In earlier heories, he radiional open economy macroeconomic heory view was adoped whereby produciviy growh is exogenous relaive o changes in he nominal exchange rae. I is only in recen years ha we see heories on endogenous produciviy dynamics emerging. Grubel (1999) and Courchene and Harris (1999) were prominen amongs hose who invesigaed he heerodox idea of he exisence of a reverse causal link, a leas in he Canada-Unied Saes (US) conex. This chaper will be organised as follows. In Secion 3.2, he plausible channels in which exchange rae movemens can affec produciviy boh negaively and posiively are discussed. Secion 3.3 looks a how asymmeries in produciviy dynamics can lead o a widening Ausralia-US produciviy gap. In Secion 3.4, oher deerminans of produciviy will be briefly discussed. Empirical evidence on his issue is reviewed in Secion 3.5. Finally, concluding remarks are given in Secion The Exchange Rae and Endogenous Produciviy Reverse Causaliy? The convenional argumen on causal linkages beween he exchange rae and produciviy focuses on he Balassa- Samuelson (B-S) hypohesis (Balassa 1964; Samuelson 1964). This is based on he radiional open macroeconomic heory ha looks upon produciviy as exogenous relaive o he nominal exchange rae as well as he exchange rae regime. The B-S hypohesis saes ha he rends in real exchange raes are accruable o differenial rends in he relaive price of non-raded goods. These differenial rends are in urn driven by differences in naional produciviy growh raes. However, he B-S heory does no offer any explanaions as o he sources of produciviy growh raes. 24

38 In recen years, he reverse causaliy issue of wheher real exchange rae depreciaions have negaive consequences on produciviy flows has been a cenral debae in Canada. This has been a resul of he subsanial depreciaion of he Canadian dollar observed in he 1990s. In paricular, Harris (2001) has argued ha exchange rae depreciaions have a negaive long run impac on labour produciviy. In urn, his may explain he widening Canada US produciviy gap. As such, Harris (2001) also argues ha he widening produciviy gap beween Canada and US can be seen as a more persisen problem for a small, open economy like Canada. Under a floaing exchange rae, i is possible for a small open economy o face a srucural ransiion problem when is major rading parner has undergone a major echnological ransiion. This argumen seems plausible for boh Canada and Ausralia. The Unied Saes has emerged as a new economy, and hence he erm old economy has been coined and applied o Ausralia, Canada as well as counries in Europe. The reverse causaliy link which posulaes ha exchange rae depreciaions can negaively influence labour produciviy appears o be a concep applicable o Ausralia given he subsanial long-erm depreciaion of he Ausralian dollar agains mos of is rading parner s currencies, as well as big swings in he Ausralian dollar relaive o rend ha have been observed since he floa in Paricularly, in he 1990s, he rade weighed Ausralian dollar (TWI) has depreciaed by more han 10 percen on 4 differen separae occasions (Macfarlane 2000). On he oher hand, auhors such as Macfarlane (2000) argues ha beliefs on Ausralia being he old economy are exremely exaggeraed and wihou fundamenal economic subsance. As such, he believes ha hese views will fade. In addiion, he also argues ha in erms of he growh of labour produciviy and muli-facor produciviy, and he exen of capial deepening, Ausralia has mached or even exceeded he remarkable US performance. However, while Ausralia s produciviy growh reached record highs in he 1990s, i does appear ha i remains in a cach- up mode from an inernaional perspecive. In fac, while Ausralia was a 83 percen of he US s produciviy level in 2003 level, his 25

39 was only slighly above where he economy was a in he 1950s (Produciviy Commission 2003). In he following sub-secions, explanaions as o how exchange rae movemens can affec produciviy negaively and posiively will be elaboraed. Figure 3.1 illusraes he hree main proposiions of a reverse causal relaionship (real exchange rae o produciviy) as well as heir associaion wih sandard of living. Figure 3.1: The Relaionship beween he Real Exchange Rae, Produciviy and Sandard of Living Facor-cos Channel Supplies and employmen raes of producive facors (labour, capial) Shelering Channel Real Exchange Rae (level) & volailiy Balassa-Samuelson Channel PRODUCTIVITY STANDARD OF LIVING Innovaion Gap Channel Technological Change Relaive Facor Cos Hypohesis This hypohesis posulaes ha exchange rae movemens affec absolue and relaive coss of capial, labour and oher facors of producion. Hence, his alers he accumulaion of differen forms of capial and relaive facor use. In accordance, labour produciviy can be affeced in wo ways. I will no only be affeced by he possible impac of he exchange rae on he acquisiion and use of new echnology, bu also by a possible shif in he 26

40 allocaion of capial and oher facors per worker. Auhors such as Lafrance and Schembri (1999) sugges ha on he whole, labour produciviy is posiively relaed o he raio of capial (and oher facors) per worker. The relaive facor cos hypohesis works by allowing for a higher cos of producion o be incurred and he subsiuion of facors of inpus o ake place. When he real exchange rae depreciaes, he cos of foreign goods, like impored machinery and equipmen, rises in real erms relaive o labour, plan and oher sources of producion for a counry like Ausralia. In such a small open economy, a real exchange rae movemen resuls in a change in he relaive price of raded and non-raded goods. This can influence he allocaion of facors of producion across he wo secors, and hence affec labour produciviy. There are several insances whereby his channel can affec capial/labour raios and hence labour produciviy. Firsly, assume here are only wo facors of producion (capial and labour) and here is always full employmen of boh facors. If he capial/labour raios in he wo secors are equal and fixed, a real exchange rae movemen ha resuls in a reallocaion of capial and labour will no affec labour produciviy. However, now assume ha raded goods are capial inensive as well. Real exchange rae depreciaions will cause relaive prices o change. As a resul, he raded goods secor will expand and he non raded goods secor will conrac. The reallocaion in resources will cause he capial/labour raios, labour produciviy levels as well as real wages o fall in boh secors. This accrues o he fac ha labour and capial will move from he relaively labour-rich and capial-poor non raded goods secor ino he relaively labour-poor and capial rich raded goods secor. If we also assume ha here is unemployed labour hen as he cos of capial goods increases, here will be a subsiuion of labour for capial in he producion process. In his case, capial/labour raios and labour produciviy will decline in boh raded and non raded secors as unemployed labour becomes employed. Hence, he real exchange rae depreciaion reduces he real wage and increases employmen in his insance. Therefore, 27

41 exchange rae depreciaions can affec he absolue and relaive coss of capial, labour and oher facors of producion. In urn, hese exchange rae movemens are able o influence produciviy Innovaion Gap Hypohesis According o he innovaion gap hypohesis proposed by Harris (2001), i has been argued ha nominal exchange rae depreciaions increase he relaive price of new echnology and of echnology workers in a counry like Ausralia. The endogenous produciviy growh model developed by Sain-Paul (1993) explains his phenomenon succincly. In his model, firms mus subsiue beween produc-enhancing aciviies such as research and developmen (R&D) and oupu expanding aciviies in he shor run. Hence, according o his view, depreciaions increase he endency for Ausralian firms o shif resources from echnology-producing aciviies o oupu expansion, slowing down overall produciviy growh. Poor performances in R&D and innovaion are plausible explanaions for a lagging produciviy growh. I has been argued ha he informaion indusries are boh key drivers of he global knowledge economy and he cenral sources of growh for modern economies (Houghon 2001). Moreover, hose OECD counries wih significan Informaion and Communicaions Technology (ICT) 7 producing secors have enjoyed he fases produciviy growh during he 1990s. Dirk and Ania (2005) have also concluded ha in counries like Finland, Ireland and Korea, close o 1 per cen of aggregae labour produciviy growh over he period was due o srong produciviy performance of he ICT manufacuring secor. Similarly, in Japan, Sweden and he Unied Saes, he ICT-producing secors have also conribued significanly o produciviy growh. Hence, 7 The ICT secor is ha par of he economy which produces informaion echnology and elecommunicaions goods and services. The ICT secor in Ausralia includes elecommunicaion services, compuer services, and seleced manufacuring and wholesale rade indusries. While some counries also include radio and TV services wihin he ICT secor, his has no been included in Ausralia. 28

42 produciviy in ICT producion appears o be a significan driver of overall produciviy growh in many developed counries. As such, he Ausralia US produciviy gap has been aribued by a number of observers o he exisence of an innovaion gap beween Ausralia and US: (i). According o Ausralian Bureau of Saisics (ABS) figures released each year, business expendiure on research and developmen (BERD) as a proporion of Ausralia s Gross Domesic Produc (GDP) had sared o fall afer a peak of 0.88 per cen in The ABS figures showed ha his proporion fell o 0.80 per cen in 1996/97, 0.67 per cen in Over he period , mos oher Organisaion for Economic Co-operaion and Developmen (OECD) counries increased heir BERD/GDP raios. In , he BERD/GDP raio fell anoher 3 per cen o $4.05 billion 15.6 per cen lower han is hisoric peak in (ABS 2000a). Vermeesch (2001) noed ha during ha period, aking R&D as a proporion of GDP, only Hungary, Ialy, Spain and Poland invesed less han Ausralia. Alhough he BERD/GDP raio increased o 0.72 and 0.78 percen in and respecively, his raio remains much lower han he peak of 0.88 percen in Moreover, Charles (2001) also noed ha even when he peak was reached in , Ausralia s BERD/GDP raio sill lagged behind he OECD average of 1.24 per cen. In addiion, he R & D inensiy of 4.2 percen for he manufacuring indusry was abou half he inensiy OECD counries were achieving. (ii). ABS figures also poined ou ha alhough business expendiure on R&D grew by 2 per cen 8 beween and , he BERD/GDP raio in curren price erms has decreased from 0.81 per cen in o 0.79 per cen in As we can see, he mos recen daa has shown us ha R&D invesmen as a proporion of GDP has declined 8 This is he figure afer he effec of changes in prices, wages and salaries removed (i.e. chain volume measures) have been aken ino accoun. 29

43 (ABS 2004). Moreover, Ausralia has no hi he peak of 0.88 per cen since and does no appear o be close o hiing i in accordance wih ABS figures. (iii) Finally, Considine, Marginson and Sheehan (2001) have shown ha while expors of knowledge inensive goods increased faser han impors beween he mid 1980s and he mid 1990s, he reverse occurred afer As Ausralia failed o inves in knowledge and knowledge-based indusries earlier on, he naion is currenly experiencing a growing rade defici in knowledge-inensive producs such as pharmaceuicals, compuing equipmen, elecommunicaions and road vehicles. According o he auhors, he defici in knowledge-inensive producs is sufficien o explain Ausralia s overall negaive rade balance and he dramaic growh of foreign deb. As a resul, i has been argued ha his defici in knowledge-inensive producs has been significan in shaping inernaional percepions of Ausralia as an old economy. Of which, his have fed ino he weakening marke posiion of he Ausralian dollar Exchange Rae Shelering Hypohesis The exchange rae shelering hypohesis is similar o he lazy manufacurers hypohesis as coined by MacCallum (1999). This hypohesis proposes ha real exchange rae depreciaions proec Ausralian firms from exernal compeiive pressures. I has been argued ha exchange rae depreciaions provide Ausralia wih a cos advanage and hence increase Ausralia s compeiiveness agains counries like he US. In fac, he exchange rae shelering mechanism is no a new idea. Porer (1998) makes he following observaion which explains he exchange rae shelering idea in a quie succinc manner. The more serious problem wih devaluaion, however, is is effec on he process of upgrading in an economy. The expecaion of a lower exchange rae leads firms owards a dependency on price compeiion and owards compeing in price-sensiive marke 30

44 segmens and indusries. Auomaion and oher forms of innovaion ha improve produciviy slow down, and he shif o higher-order compeiive advanages is rearded. The bes case for devaluaion (as a means for driving long run growh) is for naions relaively early in he sages of compeiive developmen (facor-driven or invesmendriven). Even here, however, an over reliance on policies ha arificially hold back currency appreciaion will ulimaely block advancemen (Porer 1998, p. 642). Changes in he degree of compeiiveness can influence labour produciviy hrough he following channels: Firsly, limied managerial resources may cause firms o shif invesmens from produciviy-enhancing and upgrading aciviies o oupu expansion ype of aciviies. This reduces he reallocaion of resources from low growh o high growh secors. According o Lafrance and Schembri (1999), an inheren assumpion underlying his mechanism will be ha firms managers are saisficing o a cerain exen and are no profi-maximisers. This allows managers o feel proeced from exernal forces due o he cos savings from depreciaions. As a resul, aggregae produciviy growh is slowed down. Secondly, exchange rae depreciaions can affec enry and exi decisions. Due o he cos advanage creaed by depreciaions, firms are no forced o underake upgrading, produciviy-enhancing aciviies or cu coss. As a resul, low produciviy firms ha would have exied in he absence of depreciaions remain in he indusry. In urn, hese low produciviy firms end o reduce produciviy growh. I has also been noed by Grubel (1999) ha upon depreciaion, enrans ha were previously considered marginal in conesable indusries 9 may now find i profiable o ener. This will resul in he produciviy growh in he indusry being driven down as a whole as he oupu share of he low produciviy group increases. Finally, in he case whereby economies impor echnology and capial equipmen necessary for upgrading, 9 Conesable indusries are defined as indusries ha have low enry coss. 31

45 depreciaion under he assumpion of sicky wages can possibly move from being capial inensive o labour inensive. As a resul, produciviy is lowered. While he hree channels discussed earlier argue ha exchange rae depreciaion will lead o a decline in labour produciviy in Ausralia, i should be reieraed ha i is possible for he radiional compeiiveness approach 10 o be in place in Ausralia as well. This approach emphasises he posiive impac of exchange rae depreciaion for growh. In he closed economy business cycle lieraure, a posiive demand shock can increase produciviy growh hrough several facors such as increased facor uilisaion, learningby-doing effecs, or increasing reurns o scales. More specifically, his idea emphasises ha he major sources of produciviy are oupu growh and increases in marke shares, which are driven by price compeiion. Theoreically, his idea proposes ha exchange rae depreciaions will increase inernaional price compeiiveness, hus increasing oupu growh and hence resuling in improvemens in produciviy. In addiion, he compeiiveness approach also argues ha persisen exchange rae depreciaions could resul in susained cos advanage for he counry which he currency has depreciaed, leading o improved expor performances. In urn, using sandard infan-indusry argumens, he improved expor performances can lead o an increase in produciviy. 3.3 Asymmeric Produciviy Dynamics Model The hree channels explained in he preceding secions can be incorporaed ino a model similar o ha suggesed by Harris (2002) o produce rache effecs. 11 Such a model is used in his sudy o heoreically explain how successive emporary adverse shocks (exchange rae volailiy) can resul in widening produciviy gaps beween Ausralia and he US. As such, his model will illusrae he possibiliy of a decline in Ausralia s produciviy resuling from an increase in real exchange rae volailiy. 10 As he compeiiveness approach is no he focus of his sudy, i will no be elaboraed. Furher informaion abou his convenional hypohesis can be found in Bean (1990), Burnside and Hammour (1992), Bolho (1998) and Hall (1991). 11 This model is similar o he New Economic Geography approach o core-periphery. 32

46 In he conex of his sudy, we assume ha he Core counry refers o he US and he Periphery counry refers o Ausralia. Noaions wih a * refer o he US. The following variables are defined o faciliae furher explanaion: 1. The nominal exchange rae in price noaion (E) The real exchange rae in price noaion: Q = EP * P 3. Cos Compeiiveness Index (Z) 4. Tradables produciviy in he US (A*); 5. Produciviy gap =A*-A The cos compeiiveness index (Z) for Ausralia can be defined as he raio of uni labour coss measured in a common currency. Hence, he cos compeiiveness index can be EP * expressed as Z =. In addiion, for his model, all lowercase variables denoe logs. The P compeiiveness index in Ausralia increases eiher via a real exchange rae depreciaion or an increase in relaive produciviy. This means ha he real exchange rae can also be defined in erms of produciviy: q = a*-a. 13 We also define a variable m, leing i be he marke share variable for Ausralia in he US marke. This is akin o reaing m as he relaive number of varieies of goods ha Ausralia supplies o he US, under he assumpion ha here is produc variey in his model. In 12 The nominal exchange rae in price quoaion means ha an exchange rae appreciaion causes he exchange rae o fall. 13 The echnical aspec on he derivaion of all equaions is no he focus of his sudy. More informaion on he derivaions is available in Harris (2002). 33

47 addiion, i has been assumed ha firms ha produce varieies are flexible in he very long run. Hence, m will change in response o locaion incenives. Le us assume ha: m = θ z & (3.1) In figure 3.2 below, PP represens he locus of poins in he a-q space whereby produciviy growh is saionary. Similarly, VV represens he saionary loci where r = r*. The pah SS represens he saddle pah. Figure 3.2: Asymmeric Adverse Demand Shock q V V P E * E 1 S S E 0 V V P a a* Source: Harris (2002, p. 32) We observe ha when Ausralia has a compeiive advanage, z increases leading o an increase in m, vice versa. When z = 0, here are no incenives o change he marke share of varieies produced in Ausralia. Produciviy dynamics are also assumed such ha produciviy increases come much slower han produciviy declines. Therefore, exchange rae depreciaions lead o shelering, and falling produciviy. There will be some gain in m 34

48 due o he increase in z, bu small if e is small. Hence, as q increases, VV shifs o V V. As seen in Figure 3.1, his causes produciviy (a) in Ausralia o fall. As he shock reverses, he exchange rae appreciaes (q decreases) and m sars o fall. As m falls, he V V shifs back o VV. Produciviy sars o increase, albei a a slower rae. Hence, due o asymmeries in produciviy dynamics, he ransiion phase can las for some ime. If his cycle is repeaed successively (which is wha we will expec when here is an increase in exchange rae volailiy), i is possible o observe a rise in produciviy occurring a he same ime as exchange rae depreciaions in he medium run. However, his should no be misaken as suppor for he compeiiveness approach, unless such a siuaion is observed in he long run. Hence, his model proposes ha an increase in exchange rae volailiy should lead o an overall decline in produciviy in he long run. This has also been he view suppored by Courchene (1998). The auhor argues ha large swings in he real exchange rae will cause any measures underaken o enhance produciviy o no work. In fac, a pre-requisie for he benefis of produciviy-enhancing aciviies o be fel in he long run is ha here needs o be significan exchange rae cerainy and no greaer exchange rae volailiy. 3.4 Oher Deerminans of Produciviy Apar from he channels discussed above whereby produciviy can be influenced, here are oher deerminans of produciviy. Depending on daa availabiliy, his sudy includes some of hese oher deerminans o he empirical analysis conduced in Chaper 7. In he conex of Ausralia, Dowrick (1994) finds ha an imporan facor in simulaing produciviy in Ausralia is increased openness o rade. Increased openness o rade can influence produciviy growh hrough hree channels. Firsly, i allows Ausralia o specialise in aciviies ha i has a comparaive advanage in. Secondly, here could possibly be sronger impor compeiion, which can simulae innovaion and force inefficien firms o exi he marke. Thirdly, an increase in rade can simulae knowledge 35

49 accumulaion and ransfer, which in urn can lead o an increase in innovaion, conribuing o produciviy. Specifically, he auhor found ha approximaely one-fifh of a percenage poin in he produciviy surge in he 1990s can be explained by he rade openness experienced in Ausralia a ha ime. In fac, he degree of rade openness can be seen as accruable o he micro-economic reforms underaken in Ausralia. Apar from he imporance of physical capial, human capial accumulaion has been argued o be an increasingly imporan facor in influencing produciviy. There are wo channels human capial accumulaion can affec produciviy. Firsly, labour produciviy can be increased when human capial ineracs wih oher facors of producion such as physical capial accumulaion. Secondly, he increase in skills in workers due o an increase in educaion can direcly conribue o an increase in oupu per hour. Dowrick (2003) finds ha an increase in he average schooling years by 0.8 of a year could indirecly and direcly lead o an increase in Ausralia s annual produciviy growh by a hird of a percenage poin. On he oher hand, Parham, Robers and Sun (2001) posulaes ha an invesmen in informaion echnology and elecommunicaions (ITT) is imporan in improving produciviy. According o he auhors, an increase in ITT invesmen resuls in lower ransporaion and ransacion coss and leads o a faser rae of echnological and knowledge ransfers. Colecchia and Schreyer (2001) finds ha on average, he conribuion of ITT o economic growh in Ausralia has risen from 0.27 per cen in he period o 0.79 per cen in he period. 3.5 Empirical Evidence Empirical evidence on he reverse causaliy link discussed in he preceding secions has generally looked a he Canada-US produciviy gap. In Canada, exensive formal modelling on his link comes mainly from Harris (2001). The auhor uilised daa from 18 indusries in 14 counries, from 1970 o The resuls from 36

50 he panel daa sudy found evidence for he reverse causaliy link for highly open economies. More specifically, he compeiiveness view of he posiive shor run effecs of exchange rae depreciaions on produciviy and longer erm negaive supply consequences of persisen exchange rae depreciaions on produciviy growh can been suppored. However, i has been recognised ha panel daa models impose common parameer resricions across all indusries and counries. Thus, such an empirical approach is heavily resriced, especially when exchange rae variables are concerned. On he oher hand, Srauss (1999) conduced a sudy on he B-S hypohesis. The auhor uilised a ime series muli-counry approach on 15 counries which included Canada and Ausralia. The four variables relevan o he sudy are: he real exchange rae, produciviy differenials, relaive prices of non-radables and governmen spending as a percenage of GDP. In addiion, his sudy employed hree differen economeric echniques: 1) Panel uni roo ess on real exchange rae and produciviy differenials o esablish he ime series properies of he variables in he sysem 2) Johansen s VAR, Sock and Wason s dynamic OLS and Philips and Hansen s fully modified OLS procedures o es for he robusness and sensiiviy of he esimaed resuls which were suspeced o have srong auocorrelaion 3) Granger causaliy ess and variance decomposiion mehods o invesigae feedback effecs In general, an imporan implicaion of his sudy is ha he granger causaliy ess conduced showed ha real exchange rae movemens do cause movemens in produciviy differenials in all he counries invesigaed, excep for Canada. Hence, his suggess ha produciviy differenials are no exogenous as suggesed by he B-S hypohesis bu insead, reverse causaliy may flow from real exchange rae movemens o produciviy differenials. In conras, Dupuis and Tessier (1999), as cied in Lafrance and S-Aman (1999) use a 4 variable VAR framework o examine he relaionship beween he real exchange rae and 37

51 various produciviy measures. The variables considered in he sudy include: employmen, produciviy, real wages in he manufacuring secor in Canada relaive o he US and he bilaeral real exchange rae. The analysis concludes ha once oher relevan facors ha affec produciviy are accouned for, only he B-S hypohesis is suppored. This means ha causaliy runs only from relaive produciviy levels o he real exchange rae. This is obviously quie differen from he resuls obained by Srauss (1999) ha rejecs he B-S hypohesis bu suppors he reverse causaliy link. Lafrance and Schembri (1999) explored he differen channels whereby he Canadian-US exchange rae movemens are linked o Canada s sandard of living. The channels explored (as discussed in he preceding secions) include: 1) The B-S hypohesis 2) The exchange rae shelering hypohesis 3) The facor-cos hypohesis I should be noed ha he auhors also examined he links beween exchange rae movemens and erms of rade, and how hey affec sandards of living. More specifically, i is posulaed ha a worsening of Canada s erms of rade, perhaps due o a decline in he world price of cerain Canadian-produced commodiies, will cause he exchange rae o depreciae and he Canadian sandard of living o fall. Wih regards o he B-S hypohesis channel, he auhors found ha in general, he recen cross-counry sudies on OECD counries only finds weak evidence in suppor of he hypohesis ha produciviy differenials causes changes in he real exchange rae. For he exchange rae shelering hypohesis channel, hey find lile compelling evidence o suppor he argumen ha he flexible nominal exchange rae ha has depreciaed over he 1990s has shelered he domesic indusry from foreign compeiion. Moreover, i is argued ha he correlaion beween movemens in relaive labour produciviy and he real exchange rae does no mean causaliy exiss beween hem bu raher ha his has been he 38

52 resul of a number of underlying facors like shifs in aggregae demand, which have affeced hem simulaneously. On he facor-cos hypohesis channel, he auhors agree ha by affecing absolue and relaive coss of capial, labour and oher facors of producion, exchange rae movemens can in fac lower labour produciviy as prediced heoreically. However, for he second and hird channels, he auhors noed ha he depreciaions in he real exchange rae were driven by underlying fundamenal facors. As such, hese depreciaions would have occurred even under a fixed exchange rae regime. To conclude, in he Ausralian conex, no formal economeric modelling has been conduced o invesigae he reverse causaliy link flowing from real exchange rae and is volailiy o real labour produciviy. Karunarane (2002) commened briefly on his reverse causaliy hypohesis. Using comparaive daa on labour produciviy and oal facor produciviy generaed using growh accoun mehods for boh Ausralia and US, he commened ha he hypohesis ha exchange rae depreciaions will lead o a decline in produciviy does no seem apparen in Ausralia. This needs o be invesigaed furher. 3.6 Concluding Remarks This chaper has reviewed he heoreical and empirical lieraure regarding he hypohesis ha a real exchange rae has a negaive impac on real labour produciviy. While such a reverse causal link has been widely debaed in Canada, wih some favourable saisical evidence having been esablished, no invesigaion has ye been carried ou in he Ausralian conex. Given he observed Ausralia-US produciviy gap, as well as he flucuaions in produciviy growh since he floaing of he exchange rae in 1983, here is reason for his reverse causal link o be invesigaed in he Ausralian conex. Moreover, wih he susained depreciaions ha occurred in he 1990s and he speculaion regarding he consequences of his for produciviy, i appears o be compelling o analyse he reverse causal link for Ausralia. 39

53 The empirical evidence reviewed in his chaper, mainly in he Canadian conex, appear o be ambiguous. There appears o be no general consensus as o wheher real exchange rae movemens influence produciviy in a posiive or negaive manner. More imporanly, here has also been no consensus on he exisence of he reverse causal link in Canada. As suggesed by Harris (2001), he reverse causaliy link whereby exchange rae movemens influence produciviy in a negaive manner should be subjeced o oher economeric mehods including he ime series approach, as well as differen counries daa ses in order for he link o be more formally esablished in a sysemaic empirical manner. This sudy makes is conribuion o he empirical analysis of he reverse causaliy of real exchange rae movemens/volailiy and is impac on produciviy in Chaper 7. In addiion, he plausibiliy of he exisence of a hreshold effec of real exchange rae volailiy has been accouned for o ensure he rue underlying relaionship beween he real exchange rae and produciviy has no been obscured. 40

54 CHAPTER 4: DATA DESCRIPTION 4.1 Inroducion The main aim of his chaper is o describe he daa used in his sudy. A deailed discussion is presened on he definiions, sources and measuremen of variables used in he empirical analysis of he rade and produciviy issues in Chaper 6 and Definiions, measuremen and sources of Variables All variables employed in his sudy are measured in chain volume measures. According o ABS (2000b), chain volume measures allow ime series daa for expendiure and producion aggregaes o be free of direc effecs of price changes. I is said ha when comparisons are made beween GDP esimaes in wo differen periods, he difference will reflec changes in quaniy as well as prices (inflaion). Therefore, in order o esimae only he quaniy change of GDP, he value of GDP needs o be measured using he same uni prices. When chain volume measures are no available, he variables are convered ino real variables using he relevan consumer price index (CPI). The use of real variables will ensure ha he rue underlying relaionships beween he variables are no obscured by differenial changes in prices Variables Included in he Trade Models To empirically invesigae he relaionship beween real exchange rae volailiy and real Ausralian expor volumes o US/ Japan, six economic variables are needed for each counry. As a sandard pracise in applied economerics, all series are log-ransformed. 14 For he empirical analysis on rade, daa from he pos floa period, from 1988:1 o 2005:1 is used. This sudy avoids using he firs few quarers ha come afer he floaing of he exchange rae in 1983 in order o accuraely es for he effecs of he real exchange rae 14 The log-log model enables each slope coefficien o be inerpreed as he elasiciy of he dependen variable wih respec o he explanaory variable. 41

55 volailiy afer he iniial srucural adjusmen effecs of he floa on expor volumes. As menioned by Pozo (1992), he sar of he floaing exchange rae regime is usually characerised by an explosion of volailiy. Moreover, some of he daa series are no available prior o The definiions and consrucion of hese economic variables are discussed as follows: Real Ausralian expor volumes o US/ Japan ( LEXP ) Real expor volume is defined as he value of real expors divided by he Ausralia s expor price index. However, as he expor price index for Ausralia is no available, expor uni values are used as a proxy insead. 15 Real US/Japan Gross Domesic Produc (GDP) index (1995=100) ( LINCOME ) Sandard economic heory suggess ha real income in he imporing counry is a major deerminan of an exporing naion s expors. In his sudy, he real GDP is used as a proxy for real income (Chowdhury 1993). GDP is defined as he oal value of final goods and services produced in an economy during a specified period, including any axes and minus any subsidies no included in he value of producion. In his case, real GDP is used so ha he effecs of inflaion are accouned for o esimae he acual quaniy of goods and services making up GDP. For he empirical analysis on rade, he real US/Japan GDP index is used insead, wih reference year, 1995 = 100. Relaive price of impors of he US/Japan ( LPRICE ) The relaive price of impors of he imporing counry is consruced as he raio of Ausralia s expor price index o he imporing counry s expor price index. This is also a measure for compeiiveness. 15 This has also been pracised in Chowdhury (1993). 42

56 Bilaeral Real Exchange Rae Volailiy ( LV ) Firsly, he choice beween using he real or nominal exchange rae in empirical analysis of his issue has been prominenly discussed. Due o sicky prices, i has been argued ha he real and nominal exchange rae volailiies should be he same in he shor o medium run. However, in he presence of high inflaion, nominal exchange rae volailiy is expeced o be higher han real exchange rae volailiy (Clark, P. e al. 2004). For his reason, he empirical analysis in his hesis will use measures of real exchange rae volailiy. The bilaeral real exchange rae volailiy is consruced using he sandard ARCH/GARCH mehodology. 16 In order o consruc he volailiy variables, he bilaeral real exchange rae has o be calculaed. This can be defined as he produc of he nominal exchange rae and relaive price levels in each counry. Thus, he USD/AUD real exchange rae can be defined as: RER = e Where e is he USD/AUD nominal exchange rae, p and p* are he price levels in Ausralia and US respecively. Similarly, he YEN/AUD real exchange rae can be consruced. p p * I should be noed ha in he conex of his sudy, boh he nominal and real exchange raes are expressed in a volume noaion form. This implies ha an appreciaion of he exchange rae is equivalen o an increase in he exchange rae and a depreciaion of he exchange rae is equivalen o a decrease in he exchange rae. Threshold Indicaor ( ) DUM The hreshold indicaor variable is essenially a dummy variable ha is equivalen o one when he hreshold level of real exchange rae volailiy is exceeded. The mehodology 16 This mehodology is furher elaboraed upon in Chaper 5. 43

57 employed o obain he value of he hreshold (τ ) level of real exchange rae volailiy is furher elaboraed in Chaper Ineracion ( ) INTERACTION The ineracion erm in his sudy is he produc of he bilaeral real exchange rae volailiy and hreshold indicaor. This erm specifically measures he addiional effec on expors due o real exchange rae volailiy exceeding a hreshold level. Daa Sources All daa sources for variables used direcly or in he consrucion of oher variables for he empirical sudy on rade are summarised in Table 2.1. Refer overleaf for Table Refer o chaper 5 for more informaion on he mehodology behind obaining an opimal hreshold level. 44

58 Table 2.1: Daa Sources for US/Japan Trade Models Variable Daa Source Expors o US ($m) Ausralia Bureau of Saisics (ABS) Time Series Saisics Plus in dx Daabase Expors o Japan ($m) ABS Time Series Saisics Plus in dx Daabase Expor uni values Ausralia Inernaional Moneary Fund (IMF) Inernaional Financial Saisics (IFS) daabase, available a hp://ifs.apdi.ne/imf/logon.aspx GDP Index US ABS Time Series Saisics Plus in dx Daabase GDP Index Japan ABS Time Series Saisics Plus in dx Daabase Expor price Index US IFS daabase Expor price Index Japan IFS daabase USD/AUD Nominal Exchange Rae RBA Bullein Daabase in dx Daabase YEN/AUD Nominal Exchange Rae RBA Bullein Daabase in dx Daabase CPI Ausralia TABLE C1. CPI All Iems Toal (Base year (1995=100) obained from Aussas daabase, ca. No. OECD (C) Consumer Prices CPI US TABLE C1. CPI All Iems Toal (Base year (1995=100) obained from Aussas daabase, ca. No. OECD (C) Consumer Prices CPI Japan TABLE C1. CPI All Iems Toal (Base year (1995=100) obained from Aussas, ca. No. OECD (C) Consumer Prices 45

59 4.2.2 Variables Included in he Produciviy Models There are seven variables consruced for use in he empirical analysis of he impac of real exchange rae depreciaion and volailiy on labour produciviy. The daa selecion period for his analysis ranges from 1985:3 o 2004:3. Following he argumens made above, all series are log-ransformed. The definiion and consrucion of hese economic variables are as follows: Real Labour Produciviy ( ) LLP Real labour produciviy is defined as real oupu per hour worked. Hence, his can be obained by dividing real oupu by oal acual hours worked in he Ausralian economy. In some of he earlier produciviy relaed sudies, he auhors have calculaed labour produciviy by dividing real GDP by oal labour force paricipans. However, i has since been argued ha such a measure of labour produciviy is inappropriae and misleading since he composiion of he labour force, consising of full and par-ime workers, varies over ime. Hence, if produciviy has been defined as oupu per worker, an increase in par-ime workers will decrease labour produciviy even hough oal number of hours worked in he economy remains unchanged. In his case, indusry value added oupu is used as a proxy for real oupu in Ausralia. This measure excludes inermediae inpus such as maerials, energy and services used up in he process of producion. Gross oupu can be also be used as a proxy in he measuremen of labour produciviy and is relaed o capial, labour and inermediae inpus. In he presence of ousourcing, he value-added measure is argued o give relaively more meaningful esimaes of labour produciviy compared o he gross oupu measure (Cobbold 2003). However, i should be noed ha a he aggregae level, boh measures are highly similar, differing only o he exen ha inermediae inpus are sourced from impors. 46

60 Real Capial Deepening ( ) LKD Real capial deepening (capial per hour worked) is obained by dividing oal capial sock by oal acual hours worked. Real Trade Openness ( ) LTO Real rade openness is calculaed by summing real oal expors and impors of goods and services, and dividing hem by real oupu. USD/AUD Real Exchange Rae/Volailiy, Threshold Indicaor ( LR ; LV ) These hree variables are calculaed in he same manner as described in he preceding secion, and will no be furher elaboraed in his secion. Ineracion ( ) INTERACTION The ineracion erm in his par of he sudy is he produc of he USD/AUD real exchange rae volailiy and hreshold indicaor. This erm specifically measures he addiional effec on real labour produciviy due o real exchange rae volailiy exceeding a hreshold level. Daa Sources All daa sources for variables used direcly or in he consrucion of oher variables for he empirical sudy on labour produciviy are summarised in Table 2.2. Refer overleaf for Table 2.2. In he even of a daa series used no being seasonally adjused in he original daa source, he U.S. Census Bureau s X12 seasonal adjusmen program was applied o obain he seasonally adjused daa series. Specifically, his opion was used on oal expors/impors of goods and services before he daa series can be employed in consrucing he variables needed in his sudy. As his seasonal adjusmen mehodology is a sandard saisical procedure incorporaed ino a well recognised economeric program package, such as EViews, o be readily used, he process behind i will no be furher elaboraed upon here. 47

61 Table 2.2: Daa Sources for Produciviy Models Variable Daa Source Oupu Value Added TABLE 16. Indusry Gross Value Added (cvm, $m, SA) obained from ca. No Ausralian Naional Accouns: Naional Income, Expendiure and Produc, Aussas daabase Toal capial sock TABLE 26. TRYM Capial Sock (cvm, $m, , SA) obained from Modellers Daabase, Aussas Toal hours worked ABS Labour Force Saisics Daabase in dx Daabase Toal expors of goods and services TABLE 35. TRYM expors goods and services (cvm, $m, ) obained from Modellers Daabase, Aussas Toal impors of goods and services ABS Time Series Saisics Plus in dx Daabase Nominal USD/AUD Exchange Rae RBA Bullein Daabase in dx Daabase CPI Ausralia TABLE C1. CPI All Iems Toal (Base year (1995=100) obained from ca. No. OECD (C) Consumer Prices, Aussas daabase CPI US TABLE C1. CPI All Iems Toal (Base year (1995=100) obained from ca. No. OECD (C) Consumer Prices, Aussas daabase 48

62 CHAPTER 5: MODEL SPECIFICATION AND METHODOLOGY 5.1 Inroducion This chaper presens he proposed economeric mehodology uilised o analyse he objecives saed in Chaper 1. The empirical invesigaion employs ime series economeric mehods o model wo relaionships ha are closely linked o Ausralia s sandard of living: 1) he linear and non-linear relaionship beween he real exchange rae volailiy and Ausralian expor volumes o is wo major rading parners, Japan and Unied Saes; 2) he linear and non-linear relaionship beween he real exchange rae/volailiy and Ausralian labour produciviy. As rade volumes and produciviy changes accruing from exchange rae movemens and is volailiy occur over ime, he overall effecs may be subjec o considerable lags. Hence, ime series daa are used for he economeric analysis presen in his hesis. To being our formal invesigaion of he effec of exchange rae movemens/volailiy on expor volumes/produciviy as well as o deermine he plausibiliy of a hreshold effec of exchange rae volailiy on rade volumes and produciviy, heoreical rade and produciviy models need o be specified. The models used o illusrae he wo relaionships are oulined in Secion 5.2. This secion also gives a deailed discussion of he hreshold effecs mehodology ha is incorporaed ino boh basic rade and produciviy models. Secion 5.3 briefly discusses he uni roo ess conduced in his sudy o ascerain he saionariy of he variables employed. The measuremen of exchange rae volailiy is oulined in Secion 5.4. In Secion 5.5, he mehodology behind he esing for coinegraion and he esimaion of he vecor error correcion models (VECMs) in his sudy will be discussed. Secion 5.6 involves discussing he Granger causaliy ess o ascerain he direcion of causaliy in boh he rade and produciviy models. Finally, Secion 5.7 presens he mehodology behind innovaion accouning impulse response funcions (IRFs) and Variance Decomposiions. 49

63 5.2 Model Specificaion The rade and produciviy models used in his sudy are based on he Ausralian aggregae economy. The main purpose of he wo models is o capure he implicaions of exchange rae movemens/volailiy on expor volumes and labour produciviy in Ausralia. A deailed descripion of he variables and daa sources used in his sudy can be found in Chaper The Basic Trade Model One of he objecives of his sudy involves examining he impac of exchange rae volailiy on Ausralian expor volumes o her major rading parners, he US and Japan. Hence, he model o be used in his sudy relies on he deerminans of rade proposed by inernaional rade heory. This heory implies ha rade is a funcion of foreign real income, relaive price of impors of he imporing counry, and real exchange rae volailiy, which can be expressed in he following funcional form: X = f Y, P, V ) (5.1) ( where X Y P V represens Ausralia s expor volume o US (Japan) is a measure of US s (Japan s) income (real GDP) level measures he relaive price of impors of US (Japan) measures he bilaeral USD/AUD (YEN/AUD) real exchange rae volailiy According o heoreical priors, X ough o increase as Y rises, assuming impored goods are normal goods. According o he law of demand, an increase in P should lead o a decrease in X, since he price of he US (Japan) impors are relaively more expensive han Ausralian impors. The effec of real exchange rae volailiy ( V ) on X is 50

64 ambiguous. The effecs could be negaive, posiive or indeerminae, depending on he differen kinds of heories available in he inernaional rade lieraure. 18 Equaion (5.1) can be expressed in a log-log model as follow: LEXP α 1 + β1lincome + β2lprice + β 3LV + ε1 = (5.2) where LEXP represens he (naural logarihm of) Ausralia s expor volume o US (Japan), LINCOME is a measure of (he naural logarihm of) US (Japanese) income (real GDP) level, LPRICE measures he (naural logarihm of) relaive price of impors of he US (Japan), and LV measures he (naural logarihm of) bilaeral real USD/AUD (YEN/AUD) exchange rae volailiy. As explained above, i is expeced ha he esimaed elasiciies ˆβ 1 o have a posiive sign, ˆ o have a negaive sign and βˆ o be ambiguous. β The Trade Threshold Model Boh previous heoreical and empirical sudies have failed o yield conclusive resuls on he effec of exchange rae volailiy on rade volumes. Previous empirical sudies have ried o prove one heory over he ohers. However, he empirical evidence urns ou o be largely ambiguous as well. One possible reason for he empirical ambiguiy could be due o he exisence of a hreshold effec of exchange rae volailiy. A common feaure among previous sudies has been o assume ha he effec of exchange rae volailiy on expor volumes is consan over differen degrees of volailiy. I is plausible ha risk-averse exporers behave differenly when facing differen degrees of exchange rae volailiy. 19 As such, his secion looks a he mehodology behind he hreshold models employed in his sudy. 18 Please refer o Chaper 2 for a more deailed lieraure review on his issue. 19 Chaper 2 offers a deailed explanaion on he reacion of risk adverse exporers o differen degrees of exchange rae volailiy. 51

65 Threshold Auoregressive (TAR) models are quie popular in he non-linear ime-series lieraure. Threshold models can be seen as allowing relaionships o be piece-wise linear bu globally non-linear (Tong 1983). As menioned in he preceding secion, i is likely ha exchange rae volailiy affec rade volumes only when exchange rae volailiy exceeds a cerain hreshold level. Therefore, he basic model in equaion (5.2) may lead o biased and inconsisen resuls by disregarding he possibiliy of a hreshold effec. In his hesis, a log-log model specificaion is employed due o he aracive feaure of he model ha allows us o inerpre slope coefficiens as elasiciies. The hreshold model o be used in his sudy is illusraed below: 1 LEXP = α 1 + β1lincome + β 2LPRICE + β 3 LV + ε1 (5.3) For LV τ and 2 LEXP = α 2 + β1lincome + β 2LPRICE + β 3 LV + ε 2 (5.4) For LV p τ where τ is he hreshold poin ha acivaes he effec of exchange rae volailiy on rade volumes. In his sudy, he hreshold poin is used o effecively spli he sample ino wo differen regimes: high volailiy and low volailiy. Following Hansen (2000), his sudy will use an indicaor variable DUM o capure he hreshold effec of exchange rae volailiy. Therefore, equaion (5.3) and (5.4) can hen be combined ino one equaion so ha Equaion (5.5) is: 52

66 ( DUM LV ) 2 LEXP = α 2 + β1lincome + β 2LPPRICE + β 3 LV + γ 1DUM + γ 2 + ε 2 Where 1 DUM = 0 if LV τ oherwise In he model specified in equaion (5.5), he indicaor variable DUM capures he hreshold effec. Hence, he coefficien ( γ 1 ) on he indicaor variable DUM measures if he inclusion of he hreshold effec has any impac on he exogenous volume of Ausralian expors o US/Japan. An ineracion facor ( DUM LV ) beween he indicaor variable and real exchange rae volailiy is also incorporaed. The coefficien ( γ 2 ) on he ineracion facor measures he impac of he high volailiy regime on volume of Ausralian expors o US/Japan. In he even where DUM =1, equaion (5.3) is represenaive of he siuaion implying ha a hreshold effec exiss. In his case, we see a combinaion of coefficiens from equaion (5.5) such ha we ge equaion (5.6): 2 ( α 1 + γ 1 ) + β1lincome + β 2LPRICE + ( β 3 + γ 2 ) LV + ε LEXP = 2 (5.6) I follows ha α 1 = α 2 + γ 1 and β = β 3 + γ 2. On he assumpion ha a hreshold effec exiss, he coefficiens α 1 and 1 β 3 measure he exogenous volume of Ausralian expors o he US/Japan and he impac of real exchange rae volailiy on expor volumes respecively. In addiion, α 1 should differ significanly fromα 2. Likewise, 1 β 3 should also differ significanly from 2 β 3. If no hreshold effec exis (DUM=0), he OLS model esimaion will suffice. Lasly, he hreshold model specified in Equaion (5.5) can be expressed in a simpler form as in equaion (5.7) as follows: 53

67 2 2 + β1lincome + β 2LPRICE + β 3 LV + γ 1DUM + γ 2INTERACTION ε LEXP = α + 2 (5.7) where LEXP is he (naural logarihm) of volume of real Ausralian expors o he US/Japan, LINCOME is a measure of he (naural logarihm) of he US s/japan s) income (real GDP) level, LPRICE measures he (naural logarihm) of relaive price of impors of he US/Japan), LV measures he (naural logarihm) of bilaeral USD/AUD (YEN/AUD) real exchange rae volailiy, DUM is he indicaor variable ha riggers he hreshold effec ( τ ) LV and INTERACTION measures he effec of a high volailiy regime on volume of expors o he US/Japan. This will be he equaion uilised in he empirical analysis in Chaper Esimaion of he Unknown Threshold Poin If he hreshold poin (τ ) was known, hen esimaion of equaion (5.7) becomes a simple sraighforward OLS regression. However, in his sudy, i is appropriae o assume ha we do no know he value of he hreshold poinτ. Hansen (1997) derives an esimaor for τ wih asympoic desirable properies. The auhor assumes ha when he hreshold effec is allowed o become small as he sample size increases, he asympoic disribuion of he hreshold esimaor is considered free of nuisance parameers. Therefore, his implies ha he OLS esimaes of he hreshold variable is unbiased, and can be used for inference purposes in our model. Hansen s esimaor for τ is obained by direc grid-search of plausible ranges of hreshold values. In urn, he hreshold value ha minimises he condiional sum of squared residuals and maximises he F-es saisic of he objecive equaion (5.2) 20 is hen seleced as he opimal hreshold poin for he model specified in equaion (5.7). 20 The objecive equaion refers o he basic rade model whereby we are ineresed in finding he hreshold parameer for he real exchange rae volailiy variable. 54

68 I is imporan o deermine wheher he hreshold effec is saisically significan o ensure ha he hreshold model in equaion (5.7) is indeed differen from basic model as illusraed in equaion (5.2). The hypohesis of no hreshold effec in equaion (5.6) can be represened by he following linear consrains in (5.8): H H 0 1 = α 2; : α β = β (= 0) (No hreshold) 1 1 2; 1 3 : α α β β 2 3 ( 0) (Threshold) (5.8) Assuming he errors in equaion (5.6) are independenly and idenically disribued (iid), his sudy uses he sandard F-es o es for he exisence of a hreshold effec of real exchange rae volailiy. In accordance wih he assumpion regarding he errors, Hansen (1997) posulaes ha he F-es for he exisence of a hreshold effec has near-opimal power. However, as τ is no idenified, he asympoic disribuion of he F-saisic is 2 no χ. Hansen (1996) shows ha he asympoic disribuion may be approximaed by boosrapping procedures. In accordance, a search over he values of hreshold parameers yields a sequence of F-saisics (. The maximum of hem is defined as he suprenum F- saisic ( F *). The disribuion of * converges weakly in probabiliy o he null n disribuion of F n F n. Hence, by replicaing he boosrapping procedure a sufficien number of imes (according o a simple convergence crieria), he asympoic disribuion of F n saisics 21 can be approximaed. ) F n If our esimaed F-saisic is greaer han our sandard F criical value, he null hypohesis of no hreshold effec will be rejeced. This hypohesis es is carried ou for he hreshold parameers obained for he US/Japan rade hreshold models and he produciviy hreshold model o ensure he hreshold models esimaed are appropriae. 21 This sudy uilises he WinRas 5.0 program o obain he opimal hreshold ( τ ) parameer and he appropriae F-saisic o es for he significance of he hreshold parameer. Please refer o hp:// for more informaion on he WinRas 5.0 program. 55

69 5.2.4 The Basic Produciviy Model Anoher objecive of his hesis is o invesigae he impac of exchange rae movemens and is volailiy on labour produciviy. To do his, firs assume a Cobb-Douglas producion funcion for aggregae oupu. This producion funcion assumes consan reurns o scale o all facors. 22 The basic producion funcion for aggregae oupu employed in his sudy is hus as follows: Y b ( 1 b) = AK L (5.9) where Y refers o aggregae oupu (real GDP), K is real capial sock, and L is labour and b represens he conribuion of capial o producion in he economy. By dividing boh sides of equaion (5.9) by which is used o measure labour produciviy. In his case, Y L deepening variable. Y L L, we are able o obain he following relaion K L b = A (5.10) K is a measure of real labour produciviy and is he real capial L Employing a log-log model specificaion for he produciviy model allows an inerpreaion of he slope coefficiens as elasiciies. Hence, by aking logarihms, we obain he following: 22 I is commonly argued ha parameer esimaes from a Cobb-Douglas model can be biased if he aggregae producion funcion is homogenous of degree more han one. Several auhors have esed he assumpion of consan reurns o scale in he Ausralian economy conex. For example, a an aggregae level, Valadkhani (2003) esed he consan reurns o scale assumpion, concluding ha he resuls indicae ha he null hypohesis (he sum of all producion inpu elasiciies wih respec o oupu is equal o 1) canno be rejeced. Hence, he consans reurn o scale assumpion would appear o hold for he Ausralian aggregae economy. 56

70 Y K ln = α + β 1 ln (5.11) L L Whereα is equivalen o ln A, and β1 is equivalen o b. In addiion o real physical capial sock per worker (real capial deepening), hree oher relevan variables are included in his model. Firsly, real rade openness is incorporaed ino his model o help explain labour produciviy in Ausralia which has been explained in Chaper 3. Also, since one of he objecives of his hesis is o examine he effec of real exchange rae movemens/volailiy on labour produciviy in Ausralia, he real exchange rae and he real exchange rae volailiy are included ino he model as well. The real exchange rae measures he plausible effec depreciaions could have on labour produciviy. On he oher hand, shorer run effecs of exchange rae dynamics are incorporaed ino he model hrough he exchange rae volailiy variable. Henceforh, he aggregae labour produciviy model in his sudy (in log form) is specified as follows: Where Y L K L T Y R V Y K T 1 ( R ) + β ln( V ) ε (5.12) ln L = α + β ln + β + β + L 2 ln Y 3 ln 4 measures labour produciviy. measures capial deepening is a measure for rade openness. measures real bilaeral AUD/US. is a measure for real USD/AUD volailiy In his sudy, volailiy is modelled using a GARCH (p,q) ype of models which will be furher elaboraed laer on in his chaper. 57

71 K Theoreically, i migh be expeced ha an increase in L an increase in Y L T and conribue posiively o Y. Based on Harris s (2001) ideas regarding exchange raes and produciviy, and increase (appreciaion) of R Y is expeced o cause an increase in, vice L versa. Alernaively, according o he compeiiveness 24 approach, i is also possible ha our empirical resuls could show ha a decrease in R (depreciaion) could lead o an Y increase in, vice versa. Lasly, we expec ha a decrease in L V will lead o an increase Y in. 25 Hence, he esimaed elasiciies ˆ β1, ˆ β 2 and ˆ β 3 are expeced o have posiive signs. L On he oher hand, he esimaed elasiciy βˆ 4 is expeced o be negaive. Alernaively, (5.12) can be expressed in he following simpler form which will be used for he empirical analysis in Chaper 7: LLP α β1 LKD β LTO β LR β LV + ε (5.13) = Where LLP LKD LTO measures he log of real labour produciviy measures he log of real capial deepening is a measure for log of real rade openness LR LV measures he log of USD/AUD real exchange rae is a measure for log of real USD/AUD exchange rae volailiy 24 Harris s (2001) idea and he compeiiveness approach is furher elaboraed in Chaper This is based on Harris (2002) s Core-Periphery Model ha has been furher elaboraed in Chaper 3. 58

72 5.2.5 The Produciviy Threshold Model I is also of ineres o examine wheher here is a hreshold effec of exchange rae volailiy on labour produciviy. While he basic model specified in equaion (5.13) looks a he boh he level effec and shor run dynamics of he real exchange rae, i is plausible ha he resuls obained will be biased wihou he inclusion of a hreshold effec. An esimaion of a produciviy hreshold model will enable beer inferences on a plausible reverse causaliy relaionship. As a deailed discussion on he rade hreshold model has been done in Secion and 5.2.3, we will only define he produciviy hreshold model in his secion. The heory behind he rade hreshold model is applicable o he produciviy hreshold model as well. The produciviy hreshold model o be employed in his sudy is illusraed as follows: β1lkd + β 2LTO + β 3LR + β 4 LV ε LLP = α + 1 (5.14) For LV τ and β1lkd + β 2LTO + β 3LR + β 4 LV ε LLP = α + 2 (5.15) For LV p τ τ is he hreshold poin which acivae he effec of real exchange rae volailiy on real labour produciviy. Equaions (5.14) and (5.15) can be combined ino one equaion as seen below in equaion (5.16): ( DUM LV ) β1lkd + β 2LTO + β 3LR + β 4 LV + γ 1DUM + γ 2 ε LLP = α + 2 (5.16) 59

73 1 where DUM = 0 if LV τ oherwise If DUM =1, a hreshold effec exiss. Equaion (5.14) is represenaive of his siuaion. In his case, we see a combinaion of coefficiens from equaion (5.16). This is illusraed below in equaion (5.17): 2 ( α + γ ) + β LKD + β LTO + β LR + ( β + γ ) LV + LLP ε 2 = (5.17) Therefore, expressed in a simpler form, he hreshold model o be analysed in Chaper 7 is illusraed in equaion (5.18): 2 LLP = α 2 + β1lkd + β 2LTO + β 3LR + β 4 LV + γ 1DUM + γ 2INTERACTION + ε 2 (5.18) where LLP is he (naural logarihm) of real labour produciviy, LKD measures he (naural logarihm) of real capial deepening, LTO is a measure of he (naural logarihm) of real rade openness, LR is he (naural logarihm) of USD/AUD real exchange rae, LV measures he (naural logarihm) of USD/AUD real exchange rae volailiy, DUM is he indicaor variable ha riggers he hreshold level ( τ ) LV and measures he effec of a high volailiy regime on Ausralian real labour produciviy. INTERACTION 5.3 Daa Pre-esing Uni Roo Tess Tesing he variables used in a model for saionariy has become common in economeric analysis. This is due o pioneering work done by Nelson and Plosser (1982), who found ha a large number of macroeconomic ime series appear o be non-saionary in levels, bu saionary in firs differences. A regression equaion ha conains non-saionary variables can generae spurious regression, wih high R 2 values, significan es saisics bu very 60

74 low Durbin-Wason saisics (Granger and Newbold 1974). Hence, i appears appropriae o es for he presence of uni roos and deermine he order of inegraion as a pre-es o avoid problems of spurious regressions (Engle and Granger 1987). In his sudy, variables are esed for heir saionariy using he Augmened Dickey Fuller (ADF) es. The Phillips-Perron (PP) procedure as well as he Kwiakowski, Phillips, Schmid, and Shin (KPSS) es are uilised o confirm he resuls from he ADF es. The Phillips-Perron procedure is based on srong assumpions of he error erm, whereas he KPSS es is said o have higher power han he ADF and PP ess. The uni roo ess conduced are o ascerain he saionariy of he daa series employed in his sudy. The discussions on hem are no he focus of his sudy and more informaion concerning he procedure employed in conducing hese ess can be found in Chaper 9 (daa appendices). However, i should be noed ha he hypoheses for he ADF/PP ess differ from ha of he KPSS es. The hypoheses for he ADF/PP ess are as follows: H : δ 0 (Uni roo/non-saionary) 0 = H : δ 0 (No uni roo/saionary) (5.19) 1 p And, he hypoheses o be esed using he KPSS es are: H : Uni Roo (non-saionary) 0 H 1 : No Uni Roo (saionary) (5.20) While he ADF, PP and KPSS ess will deermine he order of inegraion of he variables in his sudy, i is also essenial ha visual inspecions of he daa plos are done. 61

75 5.4 Real Exchange Rae Volailiy Measures The sandard ARCH/GARCH approach is he main mehodology employed o obain he volailiy measures for he bilaeral real exchange rae used in he empirical analysis of he produciviy and rade models in Chaper 6 and 7. Previously, common measures for volailiy include he sandard deviaion of he real exchange rae, he logarihm of he real exchange rae, saisical variance and he coefficien of variaion. 26 However, he use of hese measures of volailiy has been criicised for failing o fully capure he uncerainy in he exchange rae and ha such measures of volailiy end o be heavily ailed in heir disribuion. Also, volailiy clusering seems o be observed in he exchange rae. This means ha exchange raes display periods of unusually high volailiy followed by periods of ranquilliy (Pozo 1992). To bes capure hese feaures of he daa, he auoregressive condiional heeroskedasic (ARCH) model derived by Engle (1982) and he generalised auoregressive condiional heeroskedasic (GARCH) model derived by Bollerslev (1986) are employed in his sudy o measure/model exchange rae volailiy. The mean funcion o be used o model he condiional volailiy for boh he ARCH and GARCH models is as follows: ln R = α + ε (5.21) 0 q i= ε = β 0 + β iε i + v (5.22) Where in equaion (5.21), ln R is he firs difference of he real exchange rae expressed in logarihm. The error erm ε is normally disribued wih zero mean. The ARCH model as illusraed in Equaion (5.22) represens he condiional variance of ε given all is pas values, where v is he whie noise error erm. This model only makes sense if β 0 0 f 26 Represenaives of hese measures of volailiy in he rade lieraure include Klein (1990), Lasrapes and Koray (1990) and Chowdhury (1993). 62

76 and β i 0. If all he β i (s) are equivalen o zero, he model is lef wih he consan β 0, implying here are no dynamics in he variance equaion. As equaions (5.21) and (5.22) are bes esimaed using maximum likelihood echniques, he linear specificaion seen in equaion (5.23) can be ransformed ino a muliplicaive condiionally heeroskedasic model as proposed by Engle (1982) seen below: ε = β + β ε (5.23) 2 V 0 i i where V is he whie noise process. This sudy also implemens a generalised ARCH (p, q) model which is called he GARCH (p,q) process developed by Bollerslev (1986). This process indicaes he presence of a GARCH erm of order p, and an ARCH erm of order q. In his case, he condiional variance can now be expressed as illusraed as follows: h and as an ARMA process. The error process can be ε = V h (5.24) where σ 2 v = 1 and q p = α iε i β i i= 1 i= 1 h h α (5.25) i where V is he whie noise process. Under he ARCH/GARCH model specificaions, he condiional variance h is dependen on ime. This allows he persisence in bilaeral real exchange rae o be capured. 63

77 The GARCH model is ofen seen as a more parsimonious represenaion as compared o a high order ARCH model. The parsimonious model will have fewer coefficien resricions as compared o a high order ARCH model. In addiion, he GARCH model is also easier o esimae when coefficiens in (5.25) are all posiive. In accordance, all characerisic roos (eigenvalues) of (5.25) should lie wihin he uni circle. This sudy selecs he final ARCH/GARCH model ha bes describes he volailiy of each bilaeral real exchange rae based on: 1) Parsimony The coefficiens in he condiional variance funcion should be significan and uncorrelaed wih each oher. 2) Residual Analysis ) The sandardised residuals ( should be a whie noise process and should no conain any s ARCH or GARCH effecs if he model has been correcly specified. 3) Model Selecion Crieria In his case, we are ineresed in how well we are modelling he condiional variance funcion (raher han he mean funcion). Hence, he AIC and SIC crieria have o be modified before i can be used. The modified crieria are: AIC' = ln( L) + 2n (5.26) SIC ' = ln( L) + nln( T ) (5.27) Where L is he maximised value of he likelihood funcion, n is he number of parameers in boh he mean funcion and he condiional variance funcion, and T is he number of 64

78 observaions. Ideally, he final model chosen o mimic he condiional variance of he real bilaeral exchange rae should have a minimised AIC and SIC Mulivariae Coinegraion Analysis The finding ha many macroeconomic ime series could possibly conain a uni roo has spurred he developmen of he heory of non-saionary ime series analysis. Engle and Granger (1987) posulaes ha a linear combinaion of wo or more non-saionary ime series can be saionary. Under such circumsances, he non-saionary ime series are said o be coinegraed. Such a saionary linear combinaion represened by a coinegraing equaion can be inferred as he long run equilibrium relaionship beween he variables in he model (Sock and Wason 1988). Coinegraion analysis is essenial in his sudy o ensure ha he basic and hreshold models esimaed in he following Chapers 6 and 7 are no spurious regressions. According o Engle and Granger (1987), he original definiion of coinegraion refers o variables ha are inegraed of he same order. Two variables inegraed of differen orders canno be coinegraed. However, while he inclusion of a saionary variable is prohibied, i should no affec he remaining coefficiens in he coinegraing relaionship (as long as i is no he dependen variable). I follows ha he asympoic criical values of he es saisics should no be affeced as well. In addiion, he inclusion of dummy variables for regime changes or daa correcions are allowed alhough hese variables canno be aken as I (1) or I (0) (Engle and Granger 1991). Campbell and Perron (1991) reinforces his idea by giving a more general definiion of coinegraion. In accordance wih he definiion given by Campbell and Perron (1991), some or all he series can be rend-saionary in a model. This definiion of coinegraion does no require ha each of he imes series be inegraed of order one. The auhors 27 If he AIC and SIC crieria chooses a differen model, i should be noed ha he SIC selecs a more parsimonious model. Hence, i is imporan o visually inspec he residuals o deermine if hey are whie noise. 65

79 recognise ha in pracice, empirical research is ofen faced wih a combinaion of series ha can be eiher be I (1) or I (0). Therefore, he inclusion of saionary variables like he measures of volailiy 28 and hreshold indicaor variables 29 are no a problem in his sudy. Hence, Vecor Error Correcion Models (VECM) will be esimaed in his sudy for he basic rade and produciviy models. However, VECM(s) will no be esimaed for he hreshold models alhough coinegraion ess will be carried ou o ensure ha he hreshold models are no spurious regressions. I should also be noed ha each saionary variable included in he model could possibly cause he number of coinegraion relaionships o increase accordingly (Harris, R 1995) Johansen Coinegraion Tes This sudy uses he Johansen (1988) mehodology o es for coinegraion insead of he wo-sep procedure proposed by Engle and Granger (1987). This is mainly due o he wo-sep procedure having lower power since he procedure is prone o errors inroduced in he firs sep being carried over o he second sep of esimaion. 30 According o Kremers, Ericcson and Doldado (1992), he wo-sep procedure has a endency o rejec he null hypohesis of no coinegraion on he borderline. In addiion, when he error correcion model is esimaed using he same se of daa, he coefficien on he error adjusmen erm may be highly saisically significan. 28 Previous empirical sudies like Siregar and Rajan (2003) have found ha volailiy measures generaed using ARCH/GARCH approach could be I (0). 29 Threshold indicaor variables as defined earlier on in his chaper are essenially dummy variables ha capure he hreshold effec. As menioned, dummy variables canno be aken as I (1) or I (0) 30 This procedure involves saving he residuals from he long-run equilibrium relaionship and using i in he second sep. In he second sep, he following regression is esimaed: e ˆ = a e ˆ ε. The coefficien is obained b esimaing a regression using residuals from anoher regression. Hence, any errors from firs sep will be compounded in he second sep. a 1 66

80 The Johansen coinegraion es involves esing for he exisence of coinegraion as well as o deermine he number of coinegraing vecors in he model. This is done wihin a general n variable VAR framework. To illusrae is workings, consider he following n dimensional VAR model of order p: X = A1 X 1 + ε (5.28) So ha by differencing (5.27), he error correcion formulaion akes he form: X = Π 1 + ε (5.29) X Where = A I indicaing he number of coinegraing vecors, X and ( 1 Π 1 n ) vecors, A = ( n n) marix of parameers, I = ( n) rank ( ) = 0 1 ε are n ideniy marix. If he Π, i follows ha each elemen in ( A1 I ) mus be zero. This implies ha here is no coinegraion and all he X i processes are non-saionary (conain uni roos). Hence, X indicaes ha characerisic roos of he sysem imply convergence o long run equilibrium. Similarly, a drif erm can be included o accoun for plausibiliy of a linear rend in he daa generaing process (DGP) as seen below: 31 X = A0 + ΠX 1 + ε (5.30) In his case, he rank represens he number of coinegraing relaionships in he equaion afer daa has been derended. 31 I should be noed ha he five differen opions are available in he Johansen Coinegraion Tes in order o caer o differen ypes of daa and coinegraing relaions. Johansen (1992) provides more informaion on he specificaions of he five differen opions. 67

81 5.5.2 Specificaion of number of lags To specify he number of lags o include in he VECM, a VAR model has o be esed for he variables in he model. The selecion of he number of lags for he VECM is based on differen informaion crieria. 32 The chosen lag lengh should be minimised in more of he informaion crieria relaive o oher lag lenghs. In his sudy, he various informaion crieria are uilised up o a lag lengh of I should also be noed ha according o Lukepohl (1993), he AIC and FPE are argued o have beer properies compared o HQ and SIC. This means ha he AIC and FPE are more likely o choose he correc order more ofen as compared o HQ and SC. This sudy will uilise he auocorrelaion LM es o ensure he lag lengh chosen for he VAR model hence VECM is opimal. In addiion, he plausibiliy of he VECM(s) esimaes will be also be checked o furher ensure ha he appropriae lag lengh has been chosen for he VECM(s) Conducing he Johansen Coinegraion Tes This nex sep involves deermining which deerminisic componens o include in he VECM. Table 5.1 shows he differen alernaives available under he Johansen es. The Johansen es involves esimaing π using a reduced rank regression (Harris, R 1995). Johansen (1988) suggesed wo ess for his purpose: (i). The race es 34 λ race m () r = T ( i= r+ i ln 1 ˆ λ ) (5.31) i 32 The informaion crieria used in his paper includes: sequenial modified LR es saisic (LR), Final predicion error (FPE), Akaike (AIC), Schwarz (SIC) and Hannan-Quinn(HQ). 33 Mos researchers would sar wih a lag lengh of approximaely (Enders, 2004). In his sudy, a lag lengh of 6 appears o be appropriae. 34 The hypoheses for his es are: : 0 H : r = k, where he value of r is increased unil he null is no longer rejeced. H r vs. 1 0 = 1 T 1/ 3 68

82 (ii). The Maximum Eigenvalue es 35 max ( r r 1) = T ln( 1 ˆ λ ) λ (5.32), + r+ 1 Where λ ˆ ˆ1,... are he esimaed characerisic roos of he marixπ. λ m Table 5.1: Deerminisic componens considered in he VECM Model Characerisics Coinegraing Relaions Level of he daa 1 No inercep No deerminisic rend 2 Inercep No deerminisic rend 3 Inercep Linear rend 4 Inercep and Linear rend Linear rend 5 Inercep and Linear rend Quadraic rend Source: Based on Harris (1995, p. 92) Following Harris (1995), Model 1 is oo resricive and hence no considered as he inercep is needed o accoun for he unis of measuremen of he variables. This sudy also follows he Panula (1989) principle as suggesed by Johansen (1992), which involves esing he join hypohesis of boh he rank order and he deerminisic componens. Models consising of all combinaions of he deerminisic componens as seen in Table 5.1 are esimaed from he mos resricive Model 2 o he leas resricive Model 5. The model ha is seleced based on his echnique will be he one ha he null hypohesis is no rejeced under boh he λ race and λ max ess. 35 The hypoheses for his es are: H : r = * vs. H : r = r * 1, where he null is rejeced if he 0 r 1 + maximum Eigen value es saisic is greaer han he criical values. MacKinnon-Haug-Michelis (1999) p- values ha are provided by EViews are used insead of he criical values provided in Johansen-Juselius ables. 69

83 5.5.4 VECM Esimaion The concep of coinegraion indicaes ha a regression is no spurious bu meaningful. As menioned earlier, he saionary linear combinaion of variables imply ha here is a long run equilibrium relaionship. However, in he shor run, disequilibrium can occur in he model. Hence, according o Sargan (1984), his equilibrium error can be reaed as he error erm. In accordance, he error correcion erm (ECT) should be incorporaed ino he basic VAR, resuling in a VECM. The VECM includes variables of he VAR in firs differences, a long run relaionship esimaed in levels (from he Johansen es), and error correcion erms. A simple wo variables ( Y, X ) error correcion model can be specified as follows: Y = α X u α1 + α 2 1 ε (5.33) where is he firs difference operaor, 1 ε is he random error erm and u = Y β 0 β1 X 1, which represens he previous period error from he coinegraing relaionship. If α 2 is significan, hen i can be inerpreed as he speed adjusmen coefficien, which indicaes wha proporion of he disequilibrium in Y is correced for in he nex period. 36 The absolue value of α 2 indicaes he speed of adjusmen back o equilibrium. Therefore, i follows ha a VECM is necessary a resriced general VAR model where resricions are placed on he coefficiens (Enders 2004). 5.6 Granger Causaliy Tess The esimaed VECMs only provide informaion on he shor run dynamics and he long run equilibrium relaionship beween he variables. They do no imply ha a causal relaionship exiss. However, his sudy is also ineresed in knowing if exchange rae 36 If α 2 is equivalen o zero, his means ha changes in adjuss o changes in in he same ime period. Y X 70

84 volailiy causes changes in real expor volumes as well as real labour produciviy. Hence, his sudy conducs Granger Causaliy ess o esablish he direcion of causaliy for each variable in he models menioned earlier on. I follows ha Granger causaliy akes ino accoun he impac of pas values of a sequence x on he curren values of a sequence y, measuring wheher curren and pas values of x helps o predic fuure values of y. When he variables in a model are coinegraed, Granger causaliy ess should be carried ou in a VECM framework and no in a VAR framework where variables are firs differenced. Firs differencing negaes he useful informaion ha is found in he coinegraion relaionship(s) (Enders 2004). Granger causaliy ess conduced in a VECM framework uses he usual F es saisic, wih he null hypoheses: x granger causes y ; y granger causes x o check if here is bi-direcional causaliy. 5.7 Innovaion Accouning Impulse Response funcions This sudy also looks a he impulse response funcions and variance decomposiions of he models examined for more informaion on he shor run dynamics and he pah he variables ake o converge o he long run equilibrium. An impulse response funcion (IRF) illusraes he effec of a one-ime shock o one of he variables on curren and fuure values of he oher endogenous variables in he sysem. The IRFs illusrae he plausible pah ha each of he variables ake over ime, given a one uni exogenous shock o each of he oher variables a a paricular period. To illusrae how he impulse response funcions work, we consider he wo-variable VAR in marix form: x y a = a a + a a a x y 1 1 e + e 1 2 (5.34) 71

85 e 1 Under he assumpion ha and are uncorrelaed, hen i is easy o see ha is associaed wih x and is associaed wih y. However, if and are correlaed, a e 2 one sandard deviaion shock on causes he sysem o capure a change in y a ime, such an inerpreaion is wrong. There will be an indirec conemporaneous effec of a change in on y (resuling from a change in affecing ). e 1 e 2 +1 o be a muliplied by a change in. However, due o he correlaions in and 21 e 2 e 2 e 1 e 1 e 2 e 1 e 2 e 1 e 1 To avoid his problem in inerpreaion, he innovaions are orhogonalised via a generalised Cholesky decomposiion. This opion imposes a resricion such ha direc effec on x y has no, bu here is he presence of an indirec effec where he lagged values of affec he conemporaneous value of y. In addiion, impulse responses can change drasically upon a change in ordering of variables under Cholesky decomposiion. This sudy uses he generalised Cholesky decomposiion which according o Pesaran and Shin (1998) consrucs an orhogonal se of innovaions ha does no depend on orderings. e Variance Decomposiions According o Enders (2004), forecas error variance decomposiions illusrae how each variable moves in proporion as a resul from shocks on he variable iself, or from shocks on oher variables in he model. Variance decomposiions separae he variaion in each endogenous variable ino various componen shocks o he VAR. Hence, error variance decomposiions provide furher informaion on he sysem dynamics by illusraing he relaive imporance of each random innovaion on he impac of oher variables in he VAR. Variance decomposiions suffer from he same problem ha impulse response funcions have. Similarly, o circumven he problem, his sudy will use he Cholesky decomposiion used earlier on. Variance decomposiions wih a 20 period forecas horizon o ensure ha he variance decomposiions have converged sufficienly. 72

86 CHAPTER 6: EMPIRICAL ANALYSIS TRADE 6.1 Inroducion This chaper presens he findings of he economeric analysis concerning he relaionship beween expor volumes and real exchange rae volailiy in Ausralia. The main objecive of he economeric analysis in his chaper is o analyse he impac of real exchange rae volailiy on Ausralian expor volumes o he US and Japan. As menioned in Chaper 5, his is done by using a basic rade model and a more exensive hreshold rade model o accoun for possible hreshold effecs of real exchange rae volailiy. All he variables used in he regressions in his chaper: real expor volumes ( LEXP ), real imporing counry s income volailiy ( LINCOME ), real relaive price of impors ( LPRICE ), real exchange rae ( LV ) are expressed in naural logarihms. The analysis uses quarerly daa from he pos-floa period from 1988:1 o 2005:1. This chaper is organised as follows. Secion 6.2 presens volailiy measures for he US and Japan. Resuls from uni roo ess done o ascerain he saionariy of daa are discussed in Secion 6.3. In Secion 6.4, he resuls from he empirical analysis on he basic rade models for he US and Japan are presened. The opimal hreshold levels of real exchange rae volailiy are esimaed for he US and Japan and resuls are presened in Secion 6.5. This is followed by he empirical analysis on he hreshold rade models for US and Japan, resuls from which are discussed in Secion 6.6. In Secion 6.7, Johansen coinegraion ess are carried ou for boh he basic and hreshold rade models o ensure ha he empirical analysis in his chaper is meaningful. Secion 6.8 presens a discussion of he VECM based on he basic rade model. Furher analysis of he dynamics of he basic rade model, including impulse responses and variance decomposiions, are presened in Secion 6.9 and Concluding remarks are presened in Secion

87 6.2 Measuremen of Real Exchange Rae Volailiy As a pre-es for ARCH/GARCH errors, eyeball inspecions are done on boh he log of USD/AUD (LRUS) and YEN/AUD (LRYEN) real exchange raes. The ime plo and correlogram for LRUS are shown in Figures 6.1 and 6.2 respecively. Similarly, he ime plo and correlogram for LRYEN are shown in Figures 6.3 and 6.4 respecively..0 Figure 6.1: Time plo for LRUS LRUS From Figure 6.1, i appears ha he series LRUS shows no clear rend. The PACF as seen in he correlogram in Figure 6.2 seems o sugges ha movemens over ime in LRUS can be approximaed by possibly an AR (1) process (random walk). Furhermore, he ACF and PACF do no seem o decay, wih significan auocorrelaion sill observed a lag

88 Figure 6.2: Correlogram for LRUS Auocorrelaion Parial Correlaion AC PAC Q-Sa Prob. *******. ******* *******.* ******.* *****.* *****. * **** **** ** ***.* ** **. * Figure 6.3: Time plo for LRYEN LRYEN Similarly, for LRYEN, no clear rends can be seen from he ime plo. The correlogram for LRYEN seen in Figure 6.4 also sugges ha he series can be approximaed by a random walk process. This suggess ha boh LRUS and LRYEN are non-saionary and inegraed of one. Sandard uni roo ess conduced on LRUS and LRYEN confirms his as well. 75

89 Figure 6.4: Correlogram for LRYEN Auocorrelaion Parial Correlaion AC PAC Q-Sa Prob. *******. ******* ******.* ***** ****.* ***.* **.* *..* * * * **..* ** *** ***.. * *** **.. * Eyeball inspecions were also carried ou on he ime plos and correlograms for he firs differences of LRUS and LRYEN. The p-values of he Ljung-Box Q-saisics indicae ha firs differencing resul in saionary processes in boh series. This furher suggess ha LRUS and LRYEN are I (1) series. The presence of condiional heeroskedasiciy in he real exchange rae can be modelled using he ARCH/GARCH mehodology as explained in Chaper 5.4. The hree mean funcions ha are considered in his sudy are: 1) wih a consan; 2) wih a consan and a MA (1) erm; 3) wih a consan and rend erm. ARCH LM ess are conduced o ensure ha he mean funcion is correcly specified. The significance of he erms in he mean funcion is checked as well. The resuls are presened in Tables 6.1 and 6.2 for he US and Japan respecively. 76

90 Table 6.1: Analysis of mean funcions for USD/AUD Real Exchange rae (LRUS) Mean Funcion 0 F saisics (p-value) ln R = α + ε ( ) LM saisics (p-value) ( ) Significance of consan, MA(1) or rend erm No ln R = α 0 + ε + βε ( ) ( ) Consan No MA (1) Yes R = 0 + α1 ln α + ε ( ) ( ) Consan No Trend No As Table 6.1 indicaes, for he USD/AUD real exchange rae, he null hypohesis of no ARCH/GARCH errors is rejeced a he 5% level of significance for all hree mean funcions. However, as noed, since he rend erm does no appear o be significan, only he mean funcions wih he 1) consan; 2) consan and MA (1) erm are seleced for his sudy. Table 6.2: Analysis of mean funcions for YEN/AUD Real Exchange rae (LRYEN) Mean Funcion 0 F saisics (p-value) ln R = α + ε ( ) LM saisics (p-value) ( ) Significance of consan, MA(1) or rend erm No ln R = α 0 + ε + βε ( ) ( ) Consan No MA (1) Yes R = 0 + α1 ln α + ε ( ) ( ) Consan No Trend No Similarly, for he YEN/AUD real exchange rae, Table 6.2 indicaes ha he null of no ARCH/GARCH errors is rejeced a an approximae 10% level of significance for all hree models. Once again, he rend erm is insignifican. Hence, he same mean funcions: 1) wih consan; 2) wih consan and MA (1) erm are seleced for he case of Japan as well. 77

91 Nex, volailiy measures for he USD/AUD and YEN/AUD real exchange rae are compued. The correlograms squared residuals of he wo mean funcions seleced for he case of USD/AUD real exchange rae are visually inspeced and found in Figures 9.1 and 9.2 in Chaper 9. The p-values of he Ljung-Box Q-saisics for he firs four lags appear o be significan. This indicaes ha a condiional variance equaion wih specificaion up o ARCH (4) may be plausible. Similarly, he correlograms squared residuals of he wo mean funcions for he case of YEN/AUD shown in Figures 9.3 and 9.4 in Chaper 9 (daa appendices) are visually inspeced as well. The correlograms sugges ha ARCH (1) and (2) ype of models may be plausible for he mean funcion wih a consan. Similarly, an ARCH (1) ype of model may be suiable for he mean funcion wih a consan and MA (1) erm. Volailiy measures of various ypes (using he seleced mean funcions) up o a GARCH (2, 2) specificaion are esimaed. The modified AIC and SIC 37 are compued and abulaed in Tables 6.3 and 6.4 for he US and Japan respecively. The significance/join significance 38 of he erms in he variance equaions is also checked. The accuracy of he variance equaion is also verified by checking for whie noise in he correlograms of he sandardised residuals, as well as by conducing an ARCH LM es over 8 lags o ensure ha no ARCH errors are lef in he sandardised residuals. The null hypohesis in each case is ha here are no ARCH/GARCH errors. The relevan F-es and ARCH LM saisics from his es are also presened wih heir associaed p values in parenheses in Tables 6.3 and 6.4 overleaf. 37 The modified AIC and SIC crieria are compued according o he formulas given in Chaper 5, equaions (5.35) and (5.36). 38 The join significance of he erms is checked by conducing a Wald es, where he null hypohesis is he erms are no joinly significan. 78

92 Table 6.3: USD/AUD Volailiy Analysis Lags=4 F-saisic (p-value) ARCH LM saisic (p-value) Whie noise in residuals Ln(L) n AIC SIC Mean funcion: R ln 0 ARCH (1) ( ) ARCH (2) ( ) ARCH (3) ( ) ARCH (4) ( ) GARCH (1,1) ( ) GARCH (1,2) ( ) GARCH (2,1) ( ) GARCH (2,2) ( ) = α + ε ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Yes Yes Yes Yes Yes Yes Yes Yes Mean funcion: ln R = α 0 + ε + βε 1 ARCH (1) ( ) ARCH (2) ( ) ARCH (3) ( ) ARCH (4) ( ) GARCH (1,1) ( ) GARCH (1,2) ( ) GARCH (2,1) ( ) GARCH (2,2) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Yes Yes Yes Yes Yes Yes Yes Yes

93 According o Table 6.3, i appears ha parsimony caused us o abandon all he models wih p-values (for he F-es and LM es saisics) < 0.05 which rejec he null of no ARCH/GARCH errors as hey indicae ha hose variance equaions have no been modelled adequaely. The remaining plausible models are: GARCH (1, 1), GARCH (2, 1) and GARCH (2, 2) wih a consan erm; ARCH (2) model wih a consan, MA (1) erm. However, on furher inspecion, he ARCH (2) model wih a consan and MA (1) erm is abandoned as he firs and second order ARCH erms are neiher significan nor joinly significan according o he Wald es 39 conduced. According o Enders (2004), he AIC is biased owards selecing an over parameerised model. Therefore, using his knowledge, as well as parsimony, his sudy selecs he GARCH (2, 1) model over he GARCH (2, 2) and GARCH (1, 1) model wih a consan. Alhough he GARCH (1, 1) model minimises SIC, he second order GARCH erm in he GARCH (2, 1) model is significan and should be accouned for. Nex, as Table 6.4 indicaes, he volailiy of YEN/AUD real exchange rae series over he enire sample is bes modelled using a GARCH (2, 1) wih a significan MA (1) erm. This suggess he presence of firs and second order GARCH erms, and a firs order ARCH erm. The p-values associaed wih he F-es and LM es saisics of 0.73 and 0.70 are > 0.05 as well. Hence, he null of no ARCH/GARCH errors is no rejeced, implying ha our preferred model GARCH (2, 1) is appropriae. P-values of < 0.05 for he F-es and LM es saisics from he Wald es conduced rejec he null hypohesis. 40 This suggess ha he GARCH (1), GARCH (2) and ARCH (1) erms are joinly significanly. More imporanly, his chosen model minimises boh AIC and SIC The p-values for he F-es and chi-square saisics are and respecively. As hese p-values are > 0.05, he null hypohesis ha he ARCH (1) and ARCH (2) erms are no joinly significan canno be rejeced. 40 The null hypohesis for his Wald es is ha he GARCH (1), GARCH (2) and ARCH (1) erms are no joinly significan. 41 I should also be noed ha alhough boh he ARCH (1) models under he wo mean funcions give us a smaller AIC and SIC, he firs order ARCH erm in boh models are insignifican according o Wald ess conduced and hence he models are no considered. 80

94 Table 6.4: YEN/AUD Volailiy Analysis Lags=4 F-saisic (p-value) ARCH LM saisic (p-value) Whie noise in residuals Ln(L) n AIC SIC Mean funcion: R ln 0 ARCH (1) ( ) ARCH (2) ( ) ARCH (3) ( ) ARCH (4) ( ) GARCH (1,1) ( ) GARCH (1,2) ( ) GARCH (2,1) ( ) GARCH (2,2) ( ) = α + ε ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Yes Yes Yes Yes Yes Yes Yes Yes Mean funcion: ln R = α 0 + ε + βε 1 ARCH (1) ( ) ARCH (2) ( ) ARCH (3) ( ) ARCH (4) ( ) GARCH (1,1) ( ) GARCH (1,2) ( ) GARCH (2,1) ( ) GARCH (2,2) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Yes Yes Yes Yes Yes Yes Yes Yes

95 6.3 Tess for Saionariy of Daa The resuls of he ADF, PP and KPSS ess wih a 5 per cen level of significance are shown in Table 6.5 and 6.6 for he variables in he basic rade model for he US/Japan respecively. As his is no he focus of he hesis, and for he sake of breviy, only he final resuls of he uni roo ess are shown in he ables. The associaed criical values are in parenheses. Variable LEVELS Table 6.5: Summary Resuls of ADF, PP and KPSS ess (US) ADF sa (criical value) LEXP * ( ) LINCOME * ( ) LPRICE * ( ) LV *** ( ) FIRST DIFFERENCES PP sa (criical value) * ( ) * ( ) * ( ) * ( ) KPSS LM sa (criical value) ** ( ) ** ( ) ** ( ) *** ( ) D(LEXP) *** ( ) *** ( ) D(LINCOME) *** *** ( ) ( ) D(LPRICE) *** *** ( ) ( ) 0 Indicaes I (0) process 1 Indicaes I (1) process *Uni Roo es conduced a he No rend, no inercep specificaion ** Uni Roo es conduced a he Inercep only specificaion *** Uni Roo es conduced a he Trend and inercep specificaion ** ( ) *** ( ) *** ( ) As seen from Table 6.5, for he variables o be included in he US basic rade model, LEXP, LINCOME and LPRICE are found o be I (1) and LV is found o be I (0). I should be noed ha he null hypohesis for KPSS es is ha he variable is saionary, whereas he null hypohesis for ADF and PP ess is ha he variable has a uni roo. For he variable LV, resuls from he PP es differs from boh he ADF and KPSS ess. However, 82

96 as he KPSS es is argued o have higher power han he ADF and PP ess, LV is assumed o be I (0). The ime plos for he firs differences of LEXP, LINCOME, LPRICE and LV on levels confirms wha he uni roo ess have concluded and are found in Figure 9.5 in Chaper 9. Variable LEVELS Table 6.6: Summary Resuls of ADF, PP and KPSS ess (Japan) ADF sa (criical value) LEXP * ( ) LINCOME * ( ) LPRICE * ( ) LV *** ( ) FIRST DIFFERENCES PP sa (criical value) * ( ) * ( ) * ( ) *** ( ) KPSS LM sa (criical value) ** ( ) ** ( ) ** ( ) *** ( ) D(LEXP) * ( ) *** ( ) D(LINCOME) *** *** ( ) ( ) D(LPRICE) *** *** ( ) ( ) 0 Indicaes I (0) process 1 Indicaes I (1) process * Uni Roo es a he No rend, no inercep specificaion ** Uni Roo es a he Inercep only specificaion *** Uni Roo es a he Trend and inercep specificaion *** ( ) *** ( ) *** ( ) Similarly, as seen in Table 6.6, for he Japan basic rade model, we observe ha LEXP, LINCOME and LPRICE are I (0) and LV is I (0). The ime plos for he firs differences of LEXP, LINCOME, LPRICE and LV on levels found in Figure 9.6 in Chaper 9 confirms he uni roo ess resuls obained. 83

97 6.4 Economic Inerpreaion of he Basic Trade Models We now urn o he esimaion of he basic rade models for boh he US and Japan o invesigae he impac of bilaeral real exchange rae volailiy on Ausralian real expor volumes o US and Japan respecively. The resuls from he OLS esimaion are repored in Table 6.7. Overall, he explanaory power of he US and Japan models are relaively high, wih an R-squared of 78 per cen for US, and 77 per cen for Japan. This suggess ha saisical inferences from he wo models are relevan. The basic US/ Japan rade models esimaed in Table 6.7 follows equaion (5.3) in Chaper 5 as follows: LEXP LINCOME LPRICE = α + β1 + β 2 + β 3 + ε (5.3) LV Table 6.7: US and Japan Basic Trade Models Variables US Japan C * ( ) [ ] LINCOME * ( ) [ ] LPRICE * ( ) [ ] LV * ( ) [ ] * ( ) [ ] * ( ) [ ] * ( ) [ ] * ( ) [ ] R-square N Noes: Heeroskedasiciy-consisen Sandard errors in ( ) Heeroskedasiciy-consisen T-saisics in [ ] * Saisically significan a 5 per cen level. The T-es criical value a 5 per cen level of significance is , based on T = 68, K=3. 84

98 The resuls in Table 6.7 indicae ha he effec of changes in he imporing counry s income has a saisically significan, posiive impac on Ausralian real expor volumes o boh he US and Japan, ceeris paribus. On he oher hand, an increase in relaive price of impors of US and Japan has a saisically significan, negaive impac on real Ausralian expor volumes o boh counries. This is consisen wih our expecaions as discussed in Chaper 5 and also wih previous empirical sudies. Ineresingly, he sudy finds ha real exchange rae volailiy has a significan negaive impac on real expor volumes o he US, bu a significan posiive impac on real expor volumes o Japan. This is consisen wih he general empirical ambiguiy observed in he curren lieraure on he impac of exchange rae volailiy on rade volumes. The coefficien on LV in he US equaion implies ha he elasiciy of real Ausralian expor volumes o he US wih respec o USD/AUD real exchange rae volailiy is abou Hence, if USD/AUD real exchange rae volailiy goes up by 1 per cen, on average, he real expor volumes o he US will decline by abou 0.07 percen. For he case of Japan, he elasiciy of real Ausralian expor volumes o Japan wih respec o real YEN/AUD exchange rae is approximaely As boh elasiciies are well below one, his sudy concludes ha real expor volumes o he US and Japan are relaively unresponsive o changes in real bilaeral exchange rae. 6.5 Esimaion of he Unknown Threshold Poin ( ) τ As discussed previously in Chaper 2, real exchange rae uncerainy may need o reach a cerain level (a rigger poin) before firms reac significanly o such increased uncerainy Of course, i is no heoreically known wha his hreshold level migh be. The idenificaion of a hreshold poin is a preliminary sep o esimaing he hreshold models in he secions ha follow. Therefore, he esimaed hreshold poins for he USD/AUD and 85

99 YEN/AUD real exchange rae volailiy in equaion (5.7) were esimaed and are presened in Table Table 6.8: Esimaed Threshold Values US Japan Threshold Value F es saisic F criical value (5%) Regime 1 (Low Volailiy) Regime 2 (High Volailiy) Toal N Noe: The F-es criical value a 5% level of significance is calculaed based on T=68, J (no. of join hypoheses) =2 and K (no. of parameers in unresriced model) = 6. For boh he US and Japan, observaions wih volailiy higher han he esimaed hreshold level are assigned o regime 2 (high volailiy regime), vice versa. According o he esimaion resuls presened in Table 6.8, he null of no hreshold effec is rejeced for boh he US and Japan. Boh hreshold effecs are significan a he 5 per cen level, which is a relaively srong indicaion of he presence of a hreshold effec in boh he US and Japan for his sample. In addiion, we observe ha for approximaely 80 per cen of he ime (56 of he 68 quarers in he sample), USD/AUD real exchange rae volailiy exceeds he hreshold value. On he oher hand, he YEN/AUD real exchange rae volailiy only exceeds he hreshold value approximaely 20 per cen of he ime (13 of he 68 quarers in he sample). This could possibly be due o exporing firms in he US being more sensiive o real exchange rae volailiy changes relaive o heir counerpars in Japan. 42 Refer o Chaper 5 and he daa appendix for his chaper for an ouline on how he unknown hreshold level is deermined by WINRATS and he programming used respecively. 86

100 6.6 Economic Inerpreaion of he Trade Threshold Models Using he hreshold levels esimaed in Secion 6.5, a dummy variable and an ineracion variable was hen creaed for boh he US and Japan in order o analyse he hreshold effec of real exchange rae volailiy on Ausralian expor volumes. The dummy variable (DUM) is an indicaor ha riggers he hreshold effec when bilaeral real exchange rae exceeds or is equivalen o he hreshold value esimaed for he US and Japan in he preceding secion. The ineracion erm capures he effec of higher real exchange rae volailiy on Ausralian real expor volumes. The resuls from he OLS esimaion of he hreshold rade models are summarised in Table 6.9 below. 43 The hreshold rade models esimaed in Table 6.9 (please refer overleaf) follows equaion (5.7) in Chaper 5. The dependen variable in boh models is LEXP real expor volumes o eiher US or Japan. 2 LEXP = α 2 + β1lincome + β 2LPRICE + β 3 LV + γ 1DUM + γ 2INTERACTION + ε 2 (5.7) 43 A common problem in ime series models is ha significan serial correlaion and heeroskedasiciy are ofen presen. Hence, as par of he diagnosic esing, his sudy has also conduced he Breusch-Godfrey LM and he Whie heeroskedasiciy (no cross erms) ess o check for he exisence of serial correlaion and heeroskedasiciy. Due o a small sample size, he whie heeroskedasiciy (no cross erms) es was chosen over he es wih cross erms. Boh he basic and hreshold rade models for US and Japan appear o exhibi significan serial correlaion a he 5% level. Heeroskedasiciy is also presen in he models for US bu no in he models for Japan. Hence, auocorrelaion/heeroskedasiciy consisen -saisics are generaed using he Whie heeroskedasiciy consisen covariance marix opion in EViews (Whie, 1980). I should also be noed ha he use of he heeroskedasiciy consisen -saisics does no appear o have affeced he significance of he variables grealy as all variables reain heir significance before and afer he correcions were made. 87

101 Table 6.9: Threshold Trade Models Variables US Japan C * ( ) [ ] * ( ) [ ] LINCOME * ( ) [ ] LPRICE * ( ) [ ] LV ** ( ) [ ] DUM ** ( ) [ ] INTERACTION ** ( ) [ ] * ( ) [ ] * ( ) [ ] * ( ) [ ] * ( ) [ ] * ( ) [ ] R-square Noes: Heeroskedasiciy-Consisen Sandard errors in ( ) Heeroskedasiciy-Consisen T-saisics in [ ] * Significan a 5 per cen level. The T-es criical value a 5 per cen level of significance is , based on T = 68, K=5. ** Joinly significan using Wald Tes 44 As seen in Table 6.9, all variables are individually saisically significan a he 5 per cen level 45, excep for LV, DUM and INTERACTION in he US model. However, he Wald Tes conduced shows ha LV, DUM and INTERACTION are joinly significan in he US model. From Table 6.9, he resuls for US when here is a hreshold effec and when here is no hreshold effec can be summarised in equaion (6.1) and (6.2) respecively: 44 The p-values of (for he F-es and Chi-sq es saisics) are < 0.05, rejecing he null hypohesis ha LV, DUM and ineracion are no joinly significan. 45 The -disribuion criical value a he 5 per cen level of significance is

102 LEXP = LINCOME 1.42 LPRICE LV (6.1) when τ < LEXP = LINCOME 1.42 LPRICE LV (6.2) when τ Similarly, he resuls for Japan when here is a hreshold effec and when here is no hreshold effec can be summarised in equaion (6.3) and (6.4) respecively: LEXP = LINCOME 1.07 LPRICE LV (6.3) when τ < LEXP = LINCOME 1.07 LPRICE LV (6.4) when τ The esimaed coefficiens for he imporing counry s income and relaive price of impors have heir expeced signs and he elasiciies of hese wo variables are similar o hose in he basic rade models esimaed in Secion 6.4. As we can see, he presence of a hreshold effec has dramaically affeced he elasiciy of real expor volumes o he US wih respec o he bilaeral real exchange rae volailiy bu no for Japan. Specifically, for he US, when here is no hreshold effec, here is a 0.11 per cen increase in real expor volumes o he US as he USD/AUD real exchange rae volailiy increases by 1 per cen. This is differen from he basic US rade model esimaed in Secion 6.4 when he impac of a 1 per cen increase in USD/AUD real exchange rae volailiy resuled in a saisically significan per cen decrease in real expor volumes o US. For Japan, he exisence 89

103 of a hreshold effec has no dramaically affeced he elasiciy of real expor volumes o Japan. Specifically, a 1 per cen increase in YEN/AUD real exchange rae volailiy in he hreshold and basic models resul in a 0.04 and per cen increase in real expor volumes o Japan respecively. Table 6.9 also shows us ha boh ineracion erms are negaive and are significan/joinly significan. For he US rade hreshold model, we see ha as he USD/AUD real exchange rae volailiy exceeds he hreshold value of , he individual effec of USD/AUD real exchange rae volailiy on real expor volumes o US declines slighly from 0.11 per cen o per cen bu remains posiive. In addiion, when he USD/AUD real exchange rae eners his excessively volaile period, he significan coefficien of on DUM affecs he exogenous real expor volumes o US. As a resul, he exogenous real expor volumes changes from negaive 4.92 per cen o negaive 5.49 per cen. Therefore, for he US hreshold model, we see ha here is clearly an overall significan negaive impac on real expor volumes as a resul of he USD/AUD real exchange rae volailiy exceeding he hreshold value. For he Japan rade hreshold model, when he YEN/AUD real exchange rae volailiy exceeds he hreshold value of , he individual effec of he YEN/AUD real exchange rae volailiy on real expor volumes changes from a posiive 0.04 per cen o a negaive per cen. This clearly indicaes ha Ausralian firms exporing o Japan do respond negaively when he YEN/AUD real exchange rae is excessively volaile. The significan and negaive coefficien of on he DUM variable also indicaes ha he exogenous real expor volumes o Japan worsens, changing from a negaive 7.49 per cen o a negaive 8.26 per cen. As such, for Japan, i can also be observed when he YEN/AUD real exchange rae eners an excessively volaile period, here is an overall significan negaive impac on real expor volumes o Japan. 90

104 Therefore, he esimaion of he US and Japan hreshold rade models in his secion has clearly shown ha accouning for a hreshold effec of real exchange rae volailiy when i exiss is imporan. In boh he US and Japan cases, we see ha when real exchange rae volailiy exceeds a cerain hreshold level, here is an unambiguous significan overall negaive impac on real expor volumes o he US and Japan. 6.7 Tess for Coinegraion In order o ensure ha he basic models and hreshold models esimaed in his Chaper yields super-consisen esimaes and hence resul in a sable long run relaionship for he US and Japan, coinegraion requiremens mus be checked. I should also be noed ha as discussed in Chaper 5, he inclusion of he saionary volailiy variables and dummy variables should no affec he oher coefficiens in he coinegraing relaionships o be esimaed in his secion. Using he Johansen (1992) procedure and he Panula (1989) principle described in Chaper 5, ess for exisence of coinegraion and he appropriae coinegraion rank for boh he basic and hreshold models are performed. Before implemening he Johansen (1992) procedure, he appropriae lag lenghs mus be seleced for each model. The lag lengh is chosen based on 6 crieria: he sequenial modified LR es saisic (LR), Final predicion error (FPE), Akaike informaion crierion (AIC), Schwarz informaion crierion (SC) and Hannan-Quinn informaion crierion (HQ). The chosen lag lengh should be minimised in more of he informaion crieria relaive o oher lag lenghs. However, i should be noed ha, in he even ha here are compeing choices of lag lengh o choose from, he one ha gives us no serial correlaion will be chosen. This sudy selecs he following number of lags for he VAR (p) model: 4 lags for US basic rade model; 1 lag for Japan basic rade model; 2 lags for US hreshold model and 3 lags for Japan hreshold model. The nex sep involves esing for he exisence of coinegraion in he US and Japan basic and hreshold rade models. As explained in lengh in Chaper 5, he Johansen (1992) 91

105 principle ogeher wih he Panula (1989) principle will be employed o decide on he number of coinegraing relaions in each model and he rend specificaion o be used in esimaing he VECMs. Table 6.10 summarises he resuls from he Johansen coinegraion es conduced on he US basic rade model. Tes Table 6.10: Coinegraion Tes for US Basic Trade Model H 0 : rank = r Model 2 Model 3 Model 4 Model 5 λrace es r = ( ) r * ( ) r ( ) r ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) λmax es r = ( ) ( ) ( ) r * ( ) ( ) ( ) r ( ) ( ) ( ) r ( ) ( ) ( ) Noes: Criical values a 5% level of significance in parenheses. * Denoes non rejecion of he null hypohesis a he 5% significance level ( ) ( ) ( ) ( ) As seen, Table 6.10 repors ha he firs ime he null was no rejeced is in Model 2, wih a hypohesised rank of 1. Hence, he VECM for US ha will be esimaed in laer secions conains 1 coinegraing vecor and be based on Model 2 ha has no deerminisic rends and an inercep included in he coinegraing equaion. 92

106 For Japan, conflicing resuls are found as seen in Table 6.11 ha follows. The λ race es selecs Model 2 wih rank 1 and he λ max es selecs Model 3 wih rank 1. This sudy will selec Model 2 wih rank 1, assuming no deerminisic rends in he daa and an inercep in he coinegraing equaion as i gives us a beer inerpreaion of he coinegraing relaionship and resul in a more sable VECM as well. Tes Table 6.11: Coinegraion Tes for Japan Basic Trade Model H 0 : rank = r Model 2 Model 3 Model 4 Model 5 λrace es r = ( ) r * ( ) r ( ) r ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) λmax es r = ( ) ( ) ( ) r ( ) ( ) ( ) r * ( ) ( ) ( ) r ( ) ( ) ( ) Noes: Criical values a 5% level of significance in parenheses. * Denoes non rejecion of he null hypohesis a he 5% significance level ( ) ( ) ( ) ( ) Since he coinegraing vecor is no unique for US, a normalisaion procedure is used o normalise he firs r series (LEXP) in he vecor o an ideniy marix, so ha esimaes of he coinegraing vecor can be obained. Based on he log-likelihood funcion, he normalised coinegraing relaions for US and Japan basic rade models may be expressed as follows in equaions (6.5) and (6.6) respecively, where sandard errors are in parenheses: 93

107 For he US: ) LEXP = LINCOME LPRICE LV ( ) ( ) ( ) ( ) For Japan: ) L EXP = LINCOME LPRICE LV ( ) ( ) ( ) ( ) (6.5) (6.6) As seen above, for boh US and Japan, all he signs are as expeced. All variables are saisically significan a he 5 per cen level 46 excep for LPRICE in he model for Japan. However, i is ineresing o noe ha he magniude of response of real expor volumes in Japan o income and real exchange rae volailiy is approximaely wice ha of he US model. Hence, real exchange rae volailiy has a bigger posiive impac on real expor volumes o Japan han o US. In addiion, his sudy also finds evidence of he exisence of 2 coinegraing relaionships in boh he US and Japan hreshold rade models. 47 Hence, coinegraion requiremens have been saisfied and he models esimaed in he earlier secions provide super-consisen esimaes and he saisical inferences made are valid. I should be noed ha one of he coinegraing relaionship could possibly represen he saionary variable, LV. By inuiion, LV should coinegrae wih iself o form a saionary relaionship. The summaries of he Johansen coinegraion ess conduced for he US and Japan hreshold rade models can be found in Table 9.1 and 9.2 in Chaper VECM Esimaion This secion repors he esimaion of he VECM for he US and Japan basic rade models o observe he shor run dynamics of he basic rade models. The whie noise processes noed in he residuals and correlograms plos found in Figures 9.7 o 9.10 in Chaper 9 46 The appropriae criical value a 5 per cen level of significance is The ineracion erms for boh models have been pre-esed for saionariy. Boh erms are found o be I (1). In addiion, as noed in Chaper 5, hough dummy variables are neiher considered as I (0) nor I (1) variables, hey can be incorporaed ino coinegraing equaions. 94

108 indicaes ha he esimaed VECMs for US and Japan are adequae. In addiion, auocorrelaion LM ess (found in Tables 9.3 o 9.6 in Chaper 9) ha have been conduced on he US/Japan basic and hreshold models ensured he adequacy of he VECMs esimaed. The error-correcion models are esimaed and he adjusmen erms for he US and Japan are repored in Tables 6.12 and 6.13 respecively. Error Correcion Term (ECT) Table 6.12: Error Correcion Terms from Basic US Trade VECM Noes: Sandard errors in ( ) T-saisics in [ ] D(LEXP) D(LINCOME) D(LPRICE) D(LV) ( ) [ ] ( ) [ ] ( ) [ ] ( ) [ ] As noed in Table 6.12, he speed of adjusmen coefficiens for LEXP, LINCOME and LRPICE are significan a he 5 per cen level and he speed of adjusmen coefficien for LV is significan a he 11 per cen level 48. This offers some insigh ino he adjusmen procedure for he coinegraing relaionship for he US basic rade model discussed in Secion 6.7. Specifically, when here is a posiive disequilibrium beween LEXP, LINCOME, LPRICE and LV in his quarer, i will produce a downward pressure on LINCOME in he subsequen quarer. Such an adjusmen is logical as he decrease in income will discourage demand of Ausralian impors from he US perspecive. The coefficien of approximaely on he error adjusmen erm for LINCOME indicaes ha he adjusmen process back o equilibrium is slow. Error Correcion Term (ECT) Table 6.13: Error Correcion Terms from Basic Japan Trade VECM Noes: Sandard errors in ( ) T-saisics in [ ] D(LEXP) D(LINCOME) D(LPRICE) D(LV) ( ) ( ) ( ) ( ) [ ] [ ] [ ] [ ] 48 The -disribuion criical values (relevan for his secion) a he 5 and 11 per cen level of significance are and respecively. 95

109 Table 6.13 shows ha only he speed of adjusmen coefficiens for LV and equaions are saisically significan a he 5 per cen and 11 per cen level respecively. For Japan, a posiive disequilibrium beween LEXP, LINCOME and LV in his quarer leads o an upward pressure on LINCOME and LV in he subsequen quarer. The adjusmen coefficien of LINCOME is raher small, indicaing a slow adjusmen back o long run equilibrium. I can also be suggesed ha LV plays a larger role in resoring he sysem o is seady sae equilibrium relaive o LINCOME. The speed of adjusmen coefficiens for LPRICE in US and Japan are logical, a decrease in he relaive impor price of impors of US and Japan are expeced o lead o a decline in Ausralian expor volumes o US and Japan. In urn, his will bring he sysem back ino equilibrium. LINCOME As seen from error correcion erms, mos of he adjusmen coefficiens are small in absolue value, wih LEXP and LINCOME having he wrong signs. However, Enders (2004) noed ha his is no a problem since he Johansen ess conduced earlier on have already indicaed ha he characerisic roos of he models in his sudy imply convergence o he long run equilibrium. 6.9 Granger Non Causaliy Tess As noed in Chaper 5, he granger causaliy ess should be conduced in a VECM framework. Hence, his secion will invesigae he direcion of causaliy beween he variables in he basic US and Japan rade models. Esablishing he direcion of causaliy of variables in he models will enable policy makers o know which facors have an impac on Ausralian expors o US and Japan. As such, granger causaliy ess are performed only for he case when LEXP is he dependen variable. The direcion of causaliy beween LEXP and LV are of paricular ineres as he impac of real exchange rae volailiy on rade volumes has always been ambiguous. Tables 6.14 and 6.15 (refer o overleaf) presens he summary of he Granger Causaliy resuls conduced under he VECM framework for he US/Japan basic rade models. 96

110 Table 6.14: Granger Causaliy Resuls based on Basic US Trade VECM H F- es saisic p value 0 LINCOME does no granger cause LEXP * LPRICE does no granger cause LEXP LV does no granger cause LEXP LINCOME, LPRICE and LV does no granger cause LEXP * Saisically significan a he 10 per cen level. Table 6.15: Granger Causaliy Resuls based on Basic Japan Trade VECM H F-es saisic p value 0 LINCOME does no granger cause LEXP * LPRICE does no granger cause LEXP LV does no granger cause LEXP * LINCOME, LPRICE and LV does no granger cause LEXP * Saisically significan a he 10% level * As seen in Tables 6.14 and 6.15, only 4 causal relaionships are deeced a he 10% level of significance. For boh he US and Japan basic rade models, LINCOME is found o granger cause LEXP. This is heoreically expeced as an increase in he imporing counry s income will lead o an increase in demand for impors, ceeris paribus. The remaining 3 causal relaionships are found in he basic rade model for Japan. LPRICE is also expeced o granger cause LEXP. An increase in relaive price of impors of Japan is expeced o decrease Ausralian expor volumes o Japan. Overall, for Japan, LINCOME, 97

111 LPRICE and LV joinly granger causes LEXP. This suggess ha he OLS esimaes for Japan give an accurae view of he causal relaionship beween each independen variable and dependen variable in he sysem Innovaion Accouning Impulse Responses The IRFs presened in his sudy map ou he 24-quarers (6 years) responses of a one sandard deviaion shock o he se of innovaions on curren and fuure values of he endogenous variables: LEXP, LINCOME, LPRICE, LV. The IRFs illusraed in he 4 h column of Figures 6.5 race he effec of a one sandard deviaion shock o LV [ ( e, e, e, e )' = (0,0,0,1)'] e on curren and fuure values of all he = LEXP LINCOME LPRICE LV endogenous variables in he sysem. The calculaed impulse response funcions for he basic US and Japan rade VECMs are found in Table 6.16, wih he illusraion of hem in Figure 6.5. For breviy sake, only he firs 10 quarers responses are presened for he IRFs ables in his secion. Response of LEXP Table 6.16: IRFs for Basic US Trade VECM Period LEXP LINCOME LPRICE LV

112 Figure 6.5 Impulse Response Funcions - Basic US Trade VECM Response o Generalized One S.D. Innovaions Response of LEXP o LEXP Response of LEXP o LINCOME Response of LEXP o LPRICE Response of LEXP o LV Response of LINCOME o LEXP Response of LINCOME o LINCOME Response of LINCOME o LPRICE Response of LINCOME o LV Response of LPRICE o LEXP Response of LPRICE o LINCOME Response of LPRICE o LPRICE Response of LPRICE o LV Response of LV o LEXP Response of LV o LINCOME Response of LV o LPRICE Response of LV o LV

113 For US, he majoriy of he IRFs appear o converge o a non-zero value, indicaing ha he VECM is sable. According o Table 6.16, a one sandard deviaion shock o LV resul in an immediae response from LEXP of only Refer o overleaf for he calculaed impulse response funcions for Japan in Table 6.17, and he graphical illusraion of he impulse response funcions in Figure 6.6. According o Figure 6.6, for Japan, all he IRFs appear o have converged o a non-zero value, indicaing ha he VECM is also sable. Similar inerpreaions can be made o he IRFs. Ineresingly, a one sandard deviaion shock o LV as seen in Table 6.17 has an immediae posiive response from LEXP in he Japan model of Table 6.17: IRFs for Basic Japan Trade VECM Response of LEXP Period LEXP LINCOME LPRICE LV

114 Figure 6.6: Impulse Response Funcions Basic Japan Trade VECM Response o Generalized One S.D. Innovaions Response of LEXP o LEXP Response of LEXP o LINCOME Response of LEXP o LPRICE Response of LEXP o LV Response of LINCOME o LEXP Response of LINCOME o LINCOME Response of LINCOME o LPRICE Response of LINCOME o LV Response of LPRICE o LEXP Response of LPRICE o LINCOME Response of LPRICE o LPRICE Response of LPRICE o LV Response of LV o LEXP Response of LV o LINCOME Response of LV o LPRICE Response of LV o LV

115 In addiion, according o he IRFs ables for he US and Japan, he iniial response of LEXP o a one sandard deviaion shock o LV for 24 quarers appear o be posiive and negaive for US and Japan respecively. However, for he US, LEXP appears o respond negaively o a one sandard deviaion shock o LV saring from he 6 h quarer. For Japan, he posiive impac on LEXP appears o be declining. The observed delayed responses for boh US and Japan suggess ha real expor volumes o US and Japan may be subjeced o rade conracs in he shor run which can only be changed in he longer run. Therefore, his resul in he lagged impac observed in he behaviour of real expor volumes o US and Japan. However, he IRFs also indicae ha LEXP in he US and Japan do decline afer some ime in response o a 1 sandard deviaion posiive shock in LV. Variance Decomposiions The variance decomposiions ables for he US and Japan are presened in Tables 6.18 and 6.19 overleaf. As seen in he op row of he Tables 6.18 and 6.19 for he US and Japan respecively, when forecasing LEXP one period beyond, all of he forecas error variance is associaed wih LEXP iself. When forecasing LEXP wo periods beyond for US, we see ha approximaely 97 per cen of his forecas error variance is associaed wih LEXP, and wih approximaely 2 per cen accruing o LV. However, for he case of Japan, he corresponding value of LV is only 0.14 per cen. The ables sugges ha when forecasing LEXP, LV plays an increasing imporance for he case of Japan, bu declines in imporance in forecasing LEXP for he US. 102

116 Table 6.18: Variance Decomposiion for US Variance Decomposiion of LEXP Period S.E. LEXP LINCOME LPRICE LV Table 6.19: Variance Decomposiion for Japan Variance Decomposiion of LEXP Period S.E. LEXP LINCOME LPRICE LV

117 6.11 Concluding Remarks Similar o previous empirical sudies, Secion 6.4 in his chaper also finds ha he effecs of bilaeral real exchange rae volailiy on real expor volumes o he US and Japan are ambiguous negaive and posiive respecively. However, when he hreshold effecs of bilaeral real exchange rae volailiy have been included in he hreshold rade models, here is an unambiguous significan overall negaive impac on real expor volumes o he US and Japan. Coinegraion ess conduced in his sudy ensure ha he economic inerpreaions of he various models esimaed are no spurious regressions. The Granger causaliy ess conduced in his sudy also indicaes ha he explanaory variables LINCOME, LPRICE and LV do joinly cause changes in LEXP for Japan. On he oher hand, only LINCOME cause changes in LEXP for US. In addiion, no overall join causaliy effec is noed for US. Overall, empirical resuls from his chaper concludes ha he inclusion of hreshold effecs of real exchange rae volailiy (when here is one) in he relevan models seem o clear up he ambiguiy seen in previous empirical sudies. The failure o accoun for he hreshold effecs of real exchange rae volailiy in previous empirical sudies appears o have obscured he rue underlying relaionship beween he real exchange rae volailiy and real expor volumes. Specifically, excessive real exchange rae volailiy appears o harm real expor volumes. Moreover, he resuls from his chaper appear o provide suppor for he heories of rade hyseresis explained in Chaper 2 whereby real expor volumes are negaively affeced only afer real exchange rae volailiy exceeds a cerain hreshold value for boh he US and Japan.. In paricular, he resuls in his chaper provides evidence in suppor of he heory suggesed by Franke (1991) posulaing ha rade volumes are likely o increase when real exchange rae volailiy increases unil i reaches a cerain hreshold level. As such, his hesis suggess ha he exisence of hreshold effecs of bilaeral real exchange rae volailiy on Ausralian real expor volumes o major expor markes (like 104

118 he US and Japan) is an imporan issue policy makers in Ausralia should accoun for when formulaing rade policies. This is paricularly imporan since conrary o when he hreshold level of volailiy is riggered, an increase in bilaeral real exchange rae volailiy below he hreshold level of volailiy seems o increase real expor volumes o he US and Japan.. 105

119 CHAPTER 7: EMPIRICAL ANALYSIS PRODUCTIVITY 7.1 Inroducion The main objecive of he economeric analysis in his chaper is o analyse he impac of he real exchange rae and is volailiy on real labour produciviy in Ausralia. As menioned in Chaper 5, his is done by using a basic produciviy model and a hreshold produciviy model o accoun for possible hreshold effecs of real exchange rae volailiy. The mehods uilised in his chaper have been oulined in Chaper 5 and are similar o hose carried ou in Chaper 6. The variables used in he regressions in his chaper include: real labour produciviy ( LLP ) real capial deepening ( LKD ), real rade openness ( LTO ), he USD/AUD real exchange rae volailiy ( ( LR ) and he USD/AUD real exchange rae LV ). The empirical analysis of he relaionship beween real labour produciviy and he real exchange rae level/volailiy in Ausralia conduced in his chaper uses quarerly daa from he pos-floa period 1985:3 o 2004:3. This chaper is organised as follows. The opimal volailiy measure derived for USD/AUD real exchange rae is presened in Secion 7.2. In secion 7.3, resuls from uni roo ess on all variables in he sysem are briefly discussed. This is followed by he esimaion and economic inerpreaion of he basic produciviy model presened in Secion 7.4. In Secion 7.5, he esimaion resul of he opimal hreshold level of real USD/AUD exchange rae is presened. This is followed by a deailed empirical analysis of he produciviy hreshold model (using he opimal hreshold level obained in he preceding secion) will be discussed in Secion 7.6. In Secion 7.7, Johansen coinegraion ess are carried ou for he basic and hreshold produciviy models o ensure economic inerpreaions based on he models are plausible. Secion 7.8 presens an empirical analysis of he VECM based on he basic produciviy model. Furher analysis of he dynamics of he basic produciviy model including impulse responses and variance decomposiions are presened in Secion 7.9 and Concluding remarks are presened in Secion

120 7.2 Measuremen of USD/AUD Real Exchange Rae volailiy As a pre-es for ARCH/GARCH errors, he USD/AUD real exchange rae (LR) is visually inspeced by looking a he ime plo and correlograms for he series. The ime plo and correlograms for LR is seen in Figure 7.1 and 7.2 as follows. As seen in Figure 7.1, LR does no appear o follow a clear rend. According o Figure 7.2, even a lag 10, significan auocorrelaion which appear o follow an AR (1) process can be observed in he series. The ime plos and correlograms sugges ha LR is a non-saionary process as well. Uni roos ess were conduced and he resuls were indicaive of he presence of a uni roo in LR..0 Figure 7.1: Time plo of LR LR 107

121 Figure 7.2: Correlogram for LR Auocorrelaion Parial Correlaion AC PAC Q-Sa Prob. *******. ******* ******* ** ******. * ******.* *****.* **** *** ** ** **. * *.. ** Eyeball inspecions were also carried ou on he ime plo and correlograms of he firs differences of LR. The Ljung-Box Q saisics of he firs differences of LR indicae ha firs differencing resul in a saionary process for LR. This furher confirms ha LR is I (1). The presence of condiional heeroskedasiciy in he USD/AUD real exchange rae can be modelled using he ARCH/GARCH mehodology explained in Chaper 5.4. I should be noed ha he condiional heeroskedasiciy for he USD/AUD real exchange rae needs o be re-modelled for he analysis on labour produciviy as he sample period is differen from he analysis on rade. The hree mean funcions ha are considered in he sudy on real labour produciviy include: 1) wih a consan; 2) wih a consan and a MA (1) erm; 3) wih a consan and rend erm. ARCH LM ess are conduced o ensure ha he mean funcion is correcly specified. The significance of he erms in he mean funcion is checked as well. The resuls are summarised in Table

122 Table 7.1: Analysis of mean funcions for USD/AUD Real Exchange rae (LR) Mean Funcion R 0 F saisics (p-value) ln = α + ε ( ) LM saisics (p-value) ( ) Significance of consan, MA(1) or rend erm Consan No ln R = α 0 + ε + βε ( ) ( ) Consan No MA (1) No R = 0 + α1 ln α + ε ( ) ( ) Consan No Trend No As Table 7.1 indicaes, for he USD/AUD real exchange rae, he null hypohesis of no ARCH/GARCH errors is rejeced a he 5% level of significance for all hree mean funcions since all he p-values are < However, as he MA (1) and rend erms are insignifican, only he mean funcion wih a consan is chosen for his sudy. Nex, a variey of volailiy measures using he chosen mean funcion are esimaed up o a GARCH (2, 2) specificaion. The modified AIC and SIC 49 are compued and abulaed in Table 7.2 The significance/join significance 50 of he erms in he variance equaions is checked as well. The accuracy of he variance equaion is also verified by checking for whie noise in he correlograms of he sandardised residuals and by conducing an ARCH LM es over 8 lags o ensure ha no ARCH errors are lef in he sandardised residuals. The null hypohesis in each case is ha here are no ARCH/GARCH errors. The summary of he various ess conduced for each specificaion, he relevan F-es and ARCH LM saisics wih heir associaed p-values in parenheses are also presened in Table 7.2 on he overleaf. 49 The modified AIC and SIC crieria are compued according o he formulas given in Chaper 5, equaions (5.35) and (5.36). 50 The join significance of he erms is checked by conducing a Wald es, where he null hypohesis is he erms are no joinly significan. 109

123 Table 7.2: USD/AUD Volailiy Analysis Lags=4 F-saisic (p-value) ARCH LM saisic (p-value) Whie noise in residuals Ln(L) n AIC SIC Mean funcion: ln R 0 ARCH (1) ( ) ARCH (2) ( ) ARCH (3) ( ) ARCH (4) ( ) GARCH (1,1) ( ) GARCH (1,2) ( ) GARCH (2,1) ( ) GARCH (2,2) ( ) = α + ε ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) Yes Yes Yes Yes Yes Yes Yes Yes Firsly, he ARCH (1) model is abandoned as he p-values associaed wih he F-es and ARCH LM es saisic are < 0.05, indicaing ha he ARCH/GARCH errors have no been adequaely modelled. Using he principle of parsimony, he GARCH (1, 1) model is chosen o measure he real exchange rae volailiy in his chaper. In addiion, i should be noed ha alhough he ARCH (2) minimises boh he AIC and SIC more han he chosen model, he ARCH erms are no joinly significanly. The sandardised residuals from he GARCH (1, 1) model have been checked as noed in Table 7.2. The GARCH erms in he model have also been checked for is significan/join significance. P values of < 0.05 indicae ha he GARCH erms are joinly significan and he model is appropriae. 110

124 7.3 Tess for Saionariy of Daa Similar o Chaper 6, ADF, PP and KPSS ess are also conduced for he 5 variables relevan o he empirical analysis on Ausralian real labour produciviy. The resuls from he uni roo ess are summarised in Table 7.3. For he sake of breviy, only he final resuls are shown for boh level and firs differences (when necessary) of he variables, wih he associaed criical values in parenheses. Variable LEVELS Table 7.3: Resuls of ADF, PP and KPSS ess ADF sa (p-value) LLP * ( ) LKD * ( ) LTO * ( ) LR * ( ) LV * ( ) FIRST DIFFERENCES PP sa (p-value) * ( ) * ( ) * ( ) * ( ) * ( ) KPSS LM sa (criical value) ** ( ) ** ( ) ** ( ) ** ( ) ** ( ) *** D(LLP) 0 ( ) D(LKD) ** ( ) *** D(LTO) 0 ( ) * D(LR) 0 ( ) * D(LV) 0 ( ) 0 Indicaes I (0) process 1 Indicaes I (1) process *No rend, no inercep specificaion ** Inercep only specificaion *** Trend and inercep specificaion *** ( ) ( ) *** ( ) *** ( ) *** ( ) ** ( ) ** ( ) *** ( ) *** ( ) *** ( ) 111

125 As seen from Table 7.3, all 5 variables included in he empirical analysis on Ausralian real labour produciviy are I (1). 51 Furhermore, visual inspecion of he ime plos of he firs differences of all 5 variables (Figure 9.11 in Chaper 9) indicaes ha hey are indeed I (1). 7.4 Economic Inerpreaion of he Basic Labour Produciviy Model This secion involves he esimaion of he basic labour produciviy model as specified in equaion 5.12 in Chaper 5. This will allow economic inerpreaions o be made on he impac of he real exchange rae and is volailiy on real labour produciviy in he Ausralian conex. The resuls from he OLS esimaion are repored in Table 7.4 below. 52 The model ha is esimaed in his secion follows Equaion (5.13) in Chaper 5: LLP = + β LKD + β 2LTO + β 3LR + β 4 α 1 LV + ε (5.13) Examining he resuls for he esimaion of he basic produciviy model in Table 7.4, all he coefficiens are saisically significan a he 5 per cen level. 53 Overall, he explanaory power of he basic produciviy model is high, wih an R-squared of approximaely 98.7 per cen. Hence, saisical inference made from his model is relevan. 51 However, conflicing resuls are obained for LTO. The ADF and PP ess conclude ha LTO is I (0) and he KPSS es concludes ha i is I (1). As ADF and PP ess are argued o have low power, LTO is assumed o be I (1). 52 As explained in Chaper 6, his sudy has conduced he Breusch-Godfrey LM and he Whie heeroskedasiciy (no cross erms) ess o check for he exisence of serial correlaion and heeroskedasiciy. The basic produciviy model appears o exhibi significan serial correlaion and heeroskedasiciy a he 5% level. Hence, auocorrelaion/heeroskedasiciy consisen -saisics were generaed using he Whie heeroskedasiciy consisen covariance marix (Whie, 1980). In his insance, he use of he heeroskedasiciy consisen -saisics does no appear o have affeced he significance of he variables grealy as all variables reain heir significance before and afer he use. 53 The -disribuion criical value a he 5 per cen level of significance is

126 Table 7.4: Resuls for he Basic Labour Produciviy Model Variables Coefficiens C ( ) [ ] LKD * ( ) [ ] LTO * ( ) [ ] LR * ( ) [ ] LV * ( ) [ ] R-square N 77 Noes: Heeroskedasiciy-Consisen Sandard errors in ( ) Heeroskedasiciy-Consisen T-saisics in [ ] * Saisically significan a he 5% level In addiion, all he coefficiens excep for LR and LV have signs ha are consisen wih our expecaions for his sudy. The esimaed coefficien of LR indicaes ha an appreciaion (increase) of he real exchange rae leads o a decline in real labour produciviy in Ausralia, ceeris paribus. Such an impac is couner-inuiive o Harris (2002) s proposiion ha a real exchange rae depreciaion (appreciaion) will lead o a decrease (increase) in real labour produciviy. However, his is consisen wih he compeiiveness approach menioned in Chaper 3. This approach emphasises ha real exchange rae depreciaions lead o an increase in real labour produciviy under cerain circumsances. Alhough his sugges ha Harris (2002) s proposiion is no valid for he case of Ausralia, i is insrucive o esimae a hreshold produciviy model o ensure he rue underlying relaionship beween real exchange rae/volailiy and real labour produciviy has no been obscured. 113

127 An ineresing poin o make is ha his model has found ha here is a significan posiive effec of USD/AUD real exchange rae volailiy on real labour produciviy in Ausralia. Specifically, wih a 5 per cen level of significance, a 1 per cen increase in USD/AUD real exchange rae volailiy increases real labour produciviy by approximaely 0.07 per cen, ceeris paribus. Again, his is couner inuiive o wha has been proposed by Harris (2002) which has been elaboraed in Chaper 3. However, he posiive coefficien of LV observed is consisen wih he negaive coefficien observed on LR. This is plausible if we assume produciviy dynamics are asymmeric bu differen from wha is suggesed in Chaper 3. In his insance, we assume ha produciviy increases come much faser han produciviy declines. Hence, if real exchange rae appreciaion (increase in real exchange rae) leads o a decrease in real labour produciviy in Ausralia, we will expec an increase in real exchange rae shocks (volailiy) o lead o an increase in real labour produciviy Esimaion of he Unknown Threshold Poin ( ) τ As discussed previously, he rue underlying relaionship beween real exchange rae volailiy and labour produciviy may no be observed if here is a significan hreshold effec which is no aken ino accoun of. Hence, he inconsisen coefficiens on LR and LV observed in he model esimaed in he preceding secion could be due o he failure o ake ino consideraion he exisence of a hreshold effec of real exchange rae volailiy on real labour produciviy in Ausralia. However, similar o he empirical analysis on Ausralian expor volumes, he hreshold poin for real exchange rae volailiy is no heoreically known. Therefore, he idenificaion of a hreshold poin is a preliminary sep o esimaing he hreshold models in he secion ha follows. The esimaed opimal hreshold poin 55 for he USD/AUD real exchange rae volailiy is summarised in Table The explanaion behind his has been explained furher in Chaper Refer o Chaper 5 and he Chaper 9 for an ouline on how he unknown hreshold level is deermined by WINRATS and he programming used respecively. 114

128 Table 7.5: Resuls for he Esimaed Threshold Value Threshold Value F es saisic F criical value (5%) Regime 1 (Low Volailiy) 57 Regime 2 (High Volailiy) 19 Toal N 76 Noe: The F-es criical value a 5 per cen level of significance is calculaed based on T=76, J (no. of join hypoheses) =2 and K (no. of parameers in unresriced model) = 7. Observaions wih volailiy higher or equivalen o he esimaed hreshold level ( ) are assigned o regime 2 (high volailiy regime), vice versa. According o Table 7.5, using he curren sample size of 76 observaions, he hreshold effec for he produciviy model is saisically significan a he 5 per cen level. The F- es saisic value of srongly rejecs he null of no hreshold effec a he 5 per cen level. In addiion, we observe ha for approximaely 25 per cen of he ime (19 ou of 76 quarers in he sample), USD/AUD real exchange rae volailiy exceeds or is equivalen o he hreshold value ( ). The resuls for he esimaion of he opimal hreshold poin are found in Figure 9.12 in Chaper Economic Inerpreaion of he Produciviy Threshold Model Using he hreshold poin for USD/AUD real exchange rae volailiy found in Secion 7.5, his sudy creaes a dummy variable and an ineracion variable in order o analyse he hreshold effec of USD/AUD real exchange rae volailiy on real labour produciviy in Ausralia. The dummy variable (DUM) is an indicaor ha riggers he hreshold effec when he USD/AUD real exchange rae exceeds or is equivalen o he hreshold value of esimaed in he preceding secion. The ineracion erm capures he effec of he higher USD/AUD real exchange rae volailiy on real labour produciviy in Ausralia. The resuls from he OLS esimaion of he hreshold rade model are summarised in Table 115

129 The real labour produciviy hreshold model esimaed in his secion follows equaion (5.19) in Chaper 5 as follows: β1lkd + β 2LTO + β 3LR + β 4 LV + γ 1DUM + γ 2INTERACTION ε LLP = α + 2 (5.18) Table 7.6: Resuls for he Labour Produciviy Threshold Model Variables C ( ) [ ] LKD * ( ) [ ] LTO * ( ) [ ] LR * ( ) [ ] LV * ( ) [ ] DUM * ( ) [ ] INTERACTION * ( ) [ ] R-square Noes: Heeroskedasiciy-Consisen Sandard errors in ( ) Heeroskedasiciy-Consisen T-saisics in [ ] * Significan a 5 per cen level. 56 Significan auocorrelaion and heeroskedasiciy are found in he basic and hreshold produciviy models. Hence, auocorrelaion/heeroskedsaiciy consisen sandard errors and -saisics are generaed using he Whie heeroskedasiciy consisen covariance marix opion in EViews (Whie, 1980).I should however be noed ha he use of he heeroskedasiciy consisen -saisics does no appear o have affeced he significance of he variables grealy as all variables reain heir significance before and afer he correcions were made. 116

130 As seen in Table 7.6, all he variables in he sysem are saisically significan a he 5 per cen level. 57 The esimaed coefficiens for LKD, LTO, LR and LV have he same signs as he basic produciviy model. In addiion, he variables have highly similar magniudes o he ones in he basic produciviy model. Hence, when DUM=0 (when here is no hreshold effec), similar economic inerpreaions on hese variables from he basic produciviy model can be applied o he hreshold produciviy model as well. Overall, he explanaory power of he hreshold produciviy model is relaively high as well, wih an R-squared of approximaely 98.8 per cen. From Table 7.6, he resuls can be summarised by equaions (7.1) and (7.2) illusraing he scenario when here is no hreshold effec and when here is a hreshold effec respecively. ) LLP = LKD LTO 0.07 LR LV (7.1) when τ < ) LLP = LKD LTO 0.07 LR LV (7.2) when τ I can also been seen from Table 7.6 ha he ineracion facor is negaive and significan. This suggess ha when USD/AUD real exchange rae volailiy exceeds he hreshold value of , he individual effec of USD/AUD real exchange rae volailiy on he Ausralian real labour produciviy declines from 0.08 per cen o per cen, remaining posiive. In addiion, when he USD/AUD real exchange rae eners his excessively volaile period (when DUM=1), he significan coefficien of on DUM indicaes ha he exogenous 57 The -disribuion criical value a he 5 per cen level of significance is

131 value of Ausralian real labour produciviy is negaively affeced. In fac, when he USD/AUD real exchange rae is excessively volaile, he impac on he exogenous value of real labour produciviy in Ausralia swiches from a posiive 0.26 per cen o a negaive 0.20 per cen. If only he USD/AUD real exchange rae volailiy and is hreshold effecs are considered in isolaion, hen we see ha in periods where he USD/AUD real exchange rae exceeds he hreshold value of , he overall impac of he excessive real exchange rae volailiy is negaive. This suggess ha when he hreshold effec of USD/AUD real exchange rae volailiy is accouned for, Harris (2002) s hypohesis ha an increase in real exchange rae volailiy negaively impacs he real labour produciviy can be observed. 7.7 Tess for Coinegraion To ensure ha he basic and hreshold produciviy models have super-consisen esimaes, i is imporan o make sure ha a long run equilibrium relaionship (coinegraion) does exis among he variables in he sysem. In addiion, from earlier discussions, we know ha he inclusion of dummy variables should no affec he oher coefficiens in he coinegraing relaionships o be esimaed in his secion. In his secion, we employ he same mehods uilised in he empirical analysis on rade in Chaper 6. The appropriae lag lengh crieria considered in his chaper are: he sequenial modified LR es saisic (LR), Final predicion error (FPE), Akaike informaion crierion (AIC), Schwarz informaion crierion (SC) and Hannan-Quinn informaion crierion (HQ). As in Chaper 6, he chosen lag lengh should be minimised in more of he informaion crieria han oher lag lenghs. Therefore, wih his objecive in mind, his sudy selecs a lag lengh of 6 for he unresriced VAR model. However, i should be noed ha, in he even ha here are compeing choices of lag lengh o choose from, he one ha gives us no serial correlaion will be chosen. As such, his resuls in a lag lengh of 5 for he VECM o be esimaed in he following secion. Similarly, a lag lengh of 6 for he unresriced VAR model is found o be suiable for he hreshold produciviy model. 118

132 Table 7.7: Johansen Coinegraion Tes for he Basic Produciviy Model Tes H 0 : rank = r Model 2 Model 3 Model 4 Model 5 λrace es r = ( ) r ( ) r ( ) r ( ) r ( ) ( ) * ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) λmax es r = ( ) ( ) ( ) r * ( ) ( ) ( ) r ( ) ( ) ( ) r ( ) ( ) ( ) r ( ) ( ) ( ) Noes: Criical values a 5 per cen level of significance in parenheses. * Denoes non rejecion of he null hypohesis a he 5% significance level ( ) ( ) ( ) ( ) ( ) As seen in Table 7.7 above, conflicing resuls are found. The λ race es selecs Model 3 wih rank 1 and he λ max es selecs Model 2 wih rank 1. Due o conflicing resuls, diagnosic ess are conduced on he wo plausible models. Model 2 wih rank 1 is seleced as i performs beer in he diagnosic ess, giving us a beer inerpreaion of he coinegraing relaionship. Since he coinegraing vecor is no unique, a normalisaion procedure is used, normalising he firs r series (LLP) in he vecor o an ideniy marix, so ha esimaes of he coinegraing vecor can be obained. The normalised coinegraing relaionship, assuming he coinegraion rank =1, may be expressed in he equaion (7.3) below wih he sandard errors in parenheses: 119

133 L LP ) = LKD LTO LR LV ( ) ( ) ( ) ( ) ( ) (7.3) As can be seen from above, real capial deepening (LKD) was found o have a counerinuiive negaive effec alhough no surprisingly his coefficien is saisically insignifican a he 5 per cen level. 58 In addiion, LV is found o be saisically insignifican as well. All he variables have signs ha are similar o wha has been obained from he OLS esimaes discussed in he earlier secions. I can be suggesed ha he insignificance as well as wrong sign of LKD may be due o real capial deepening no being a significan facor in affecing real labour produciviy in Ausralia. In paricular, human capial insead of physical capial may be a beer conribuing facor o real labour produciviy in Ausralia. As noed by Dowrick (2003), if he average years of schooling of young people in Ausralia increases by 1 year, real GDP would increase by up o 8 per cen over an approximae ime span of 40 years. Hence, his conribues o an increase in real labour produciviy in Ausralia. On he oher hand, resuls from he coinegraing equaion suppors he compeiiveness view of negaive (posiive) effecs of real exchange rae appreciaion (depreciaion) on real labour produciviy. As such, his implies ha he compeiiveness approach is valid in he longer erm as well, which opposes he view ha exchange rae depreciaions will have a negaive impac on labour produciviy in he longer erm. Overall, a long erm equilibrium relaionship is found beween LLP, LTO and LR. The insignifican LV erm is consisen wih he possibiliy ha he hreshold effec of LV needs o be aken ino accoun of before we observe a significan impac of LV on LLP. The Johansen (1992) coinegraion es has also been conduced on he hreshold produciviy model o ensure ha he economic inerpreaions made on he model 58 The -disribuion criical value a 5 per cen level of significance is

134 esimaed in Secion 7.6 are plausible ones. 59 This sudy finds coinegraion in he hreshold produciviy model, wih a possibiliy of he exisence of 6 coinegraing relaions using Model 3. The Johansen coinegraion es for he produciviy hreshold model is summarised in Table 9.7 in Chaper VECM Esimaion The esimaion of he VECM for he basic produciviy model is presened in his secion. Due o ime consrains, he VECM for he hreshold produciviy model will no be covered in his hesis and is lef as an area for furher research. Residuals and correlograms plos which can be found in Figures 9.13 and 9.14 in Chaper 9 appear o be generally ha of whie noise processes as well. This indicaes ha he esimaed VECM is adequae and sable. Furher confirmaion can be found in he LM auocorrelaion es conduced for he basic produciviy model can be found in Table 9.11 in Chaper 9. Error Correcion Term (ECT) Table 7.8: Error Correcion Terms from Basic Produciviy VECM Noes: Sandard errors in ( ) T-saisics in [ ] D(LLP) D(LKD) D(LTO) D(LR) D(LV) ( ) ( ) ( ) ( ) ( ) [ ] [ ] [ ] [ ] [ ] As seen in Table 7.8, he speed of adjusmen coefficiens for LTO and LV are saisically significan a he 5 per cen level. 60 Therefore, when here is a posiive disequilibrium beween LLP, LKD, LTO, LR and LV in his quarer, LTO will decrease and LV will increase in he nex quarer o bring he sysem back ino long run equilibrium. As we can see, such an adjusmen in LTO is logical as a decrease in rade openness will decrease rade volumes and hence real GDP. This will lead o a decline in real labour produciviy in Ausralia, bringing he sysem back ino equilibrium. On he oher hand, he posiive and 59 The ineracion erm for he hreshold produciviy model has been pre-esed for saionariy. I is found o be I (1). In addiion, as noed in Chaper 5, alhough dummy variables are neiher considered as I (0) nor I (1) variables, hey can be incorporaed ino coinegraing equaions. 60 The -disribuion criical value (relevan for his secion) a he 5% level of significance is

135 significan adjusmen coefficien on LV suggess ha an increase in LV will bring labour produciviy back o long run equilibrium which is plausible if Harris (2002) s approach is adoped. Alhough he coefficien on D (LR) is insignifican, he sign on i is logical. By applicaion of he compeiiveness approach in he shor run, a posiive disequilibrium in LLP should lead o an appreciaion of he real exchange rae (an increase in real exchange rae) in order o reduce he real labour produciviy. 7.9 Granger Non Causaliy Tess Granger causaliy ess are essenial for he empirical analysis on real labour produciviy in Ausralia in order o deermine if a bi-causaliy effec runs beween USD/AUD real exchange rae movemens/volailiy and real labour produciviy in Ausralia. Table 7.9: Granger Causaliy Resuls based on Basic Produciviy VECM H Chi-sq es saisic p-value 0 LR does no granger cause LLP LV does no granger cause LLP LLP does no granger cause LR * * Saisically significan a he 5% level. The granger causaliy ess conduced and summarised in Table 7.9 above indicaes ha he USD/AUD real exchange rae and is volailiy does no appear o conribue o real labour produciviy in any manner on an aggregae basis. However, his sudy finds uni-causaliy running from LLP o LR. This is prediced by he Balassa-Samuelson hypohesis ha saes ha rends in real exchange raes are accruable o differenial rends in relaive price of non-raded goods. In urn, hese differenial rends in relaive prices are driven by differences in naional produciviy growh raes. 122

136 While bi-causaliy effecs do no appear o run in he aggregae economy, i is plausible o find bi-causaliy effecs in he raded/non-raded secors and individual secors of he economy. This hesis has only conduced he sudy based on he aggregae economy. As such, i appears insrucive o conduc similar ess on differen secors of he Ausralian economy Innovaion Accouning Impulse Response Funcions The impulse response funcions (IRFs) are illusraed in Figure 7.3, wih he abulaed impulse response funcions found in Table The impulse response funcions presened in Figure 7.3 map ou he 24-quarers (6 years) responses of a one sandard deviaion shock o he se of innovaions on curren and fuure values of he endogenous variables: LLP, LKD, LTO, LR, LV. As seen from Figure 7.3, a one sandard deviaion posiive shock o LLP and LKD seems o affec LLP and LKD significanly and permanenly, vice versa for a period longer han 24 quarers. Ineresingly, he abulaed impulse response funcions as seen in Table 7.10 ells us ha wih a one sandard deviaion posiive shock in LR, he response of LLP is negaive for he firs 9 quarers and posiive hereafer. For breviy sake, only he firs 12 quarers of he abulaed impulse response funcions will be shown in Table The impulse responses appear o be generally consisen wih Harris (2001) s hypohesis in he longer run which saes ha real exchange rae depreciaions (appreciaions) will have a negaive (posiive) impac on real labour produciviy. This resul is also consisen wih he empirical analysis Harris (2001) who finds ha recen exchange rae depreciaions (appreciaions) acually increases (decreases) labour produciviy. However, longer erm deviaions from he purchasing power pariy (as a misalignmen measure) worsen i (improves i), boh by relaively small amouns. 123

137 -.02 Figure 7.3: Impulse Response Funcions Basic Produciviy VECM Response o Generalized One S.D. Innov aions Response of LLP o LLP Response of LLP o LKD Response of LLP o LTO Response of LLP o LR Response of LLP o LV Response of LKD o LLP Response of LKD o LKD Response of LKD o LTO Response of LKD o LR Response of LKD o LV Response of LTO o LLP Response of LTO o LKD Response of LTO o LTO Response of LTO o LR Response of LTO o LV Response of LR o LLP Response of LR o LKD Response of LR o LTO Response of LR o LR Response of LR o LV Response of LV o LLP Response of LV o LKD Response of LV o LTO Response of LV o LR Response of LV o LV

138 Table 7.10: Tabulaed Impulse Response Funcions Response of LLP Period LLP LKD LTO LR LV E Variance Decomposiions Similar o Chaper 6 s analysis, variance decomposiions for he empirical analysis on real labour produciviy are analysed as well. Inspecion of Figure 7.4 shows ha he resuls of he variance decomposiions are mosly consisen wih he granger causaliy ess conduced earlier on. For insance, he proporion of variance in LTO is due is own innovaions and variaions in LR (real exchange rae), which he granger causaliy es conduced in his chaper has found ha LR does granger cause LTO. On he oher hand, he proporion of variance in LLP is largely due o is own innovaions, wih a small proporion accouned for by variaions in LR (real exchange rae) in he laer quarers. While granger causaliy ess conduced earlier on are unable o find a link flowing from LR o LLP, he variance decomposiions seem o sugges ha real exchange rae does significanly influence he variance in LLP o a small exen in he laer quarers. 125

139 0 Figure 7.4: Variance Decomposiions US basic produciviy VECM Variance Decomposiion Percen LLP variance due o LLP Percen LLP variance due o LKD Percen LLP variance due o LTO Percen LLP variance due o LR Percen LLP variance due o LV Percen LKD variance due o LLP Percen LKD variance due o LKD Percen LKD variance due o LTO Percen LKD variance due o LR Percen LKD variance due o LV Percen LTO variance due o LLP Percen LTO variance due o LKD Percen LTO variance due o LTO Percen LTO variance due o LR Percen LTO variance due o LV Percen LR variance due o LLP Percen LR variance due o LKD Percen LR variance due o LTO Percen LR variance due o LR Percen LR variance due o LV Percen LV variance due o LLP Percen LV variance due o LKD Percen LV variance due o LTO Percen LV variance due o LR Percen LV variance due o LV

140 Table 7.11: Tabulaed Variance Decomposiions US basic produciviy VECM Variance Decomposiion of LLP Period S.E. LLP LKD LTO LR LV Concluding Remarks In his chaper, he relaionship beween he USD/AUD real exchange rae/volailiy and real labour produciviy in he Ausralian aggregae economy has been analysed by using a basic produciviy model and a hreshold produciviy model. Using he basic produciviy model, his sudy finds ha an appreciaion (depreciaion) of he USD/AUD real exchange rae has a negaive (posiive) impac on real labour produciviy in Ausralia, consisen wih he compeiiveness approach. Similarly, an increase (decrease) in he USD/AUD real exchange rae volailiy has a posiive (negaive) influence on real labour produciviy in Ausralia. According o he coinegraion es conduced, here is a significan long run equilibrium relaionship beween he variables in he basic produciviy model. However, real exchange rae volailiy (LV) and real capial deepening (LKD) are found o be insignifican in explaining real labour produciviy in Ausralia. On he oher hand, an appreciaion of he real exchange rae has a negaive impac on real labour produciviy in Ausralia, which is consisen wih he OLS esimaes obained in he basic produciviy model. Hence, he 127

141 implicaion of his has been ha Harris (2001) s proposiion which has been argued for he case of Canada does no seem applicable o he Ausralian aggregae economy. Insead, he compeiiveness approach ha saes ha a real exchange rae appreciaion (depreciaion) will impac he real labour produciviy in a negaive (posiive) manner seems applicable. The Granger causaliy es conduced in his chaper also finds no evidence of he USD/AUD real exchange rae/volailiy influencing real labour produciviy in Ausralia. Insead, he resuls from he Granger causaliy es appear o provide evidence in suppor of he Balassa-Samuelson hypohesis in Ausralia. This means ha rends in real exchange raes are accruable o differenial rends in relaive price of non-raded goods. In urn, hese differenial rends in relaive prices are driven by differences in naional produciviy growh raes. In addiion, he hreshold effec of USD/AUD real exchange rae volailiy has also been incorporaed and esimaed. Srong evidence poins o he exisence of a significan hreshold effec of USD/AUD real exchange rae volailiy. The resuls from he real labour produciviy hreshold model esimaed in his sudy can be summarised as follows. If only he USD/AUD real exchange rae volailiy and is hreshold effecs are considered in isolaion, hen we see ha in periods where he USD/AUD real exchange rae exceeds he hreshold value of , he overall impac of he excessive real exchange rae volailiy is negaive. In urn, his implies ha Harris (2002) s hypohesis appears o be only applicable when he USD/AUD real exchange rae is excessively volaile. Wih he decline in real labour produciviy in Ausralia in recen years, furher research on he link beween real exchange rae movemens/volailiy and real labour produciviy in he Ausralian aggregae economy and he various individual secors is warraned. While previous micro-economic reforms have helped raise real labour produciviy levels in Ausralia, he resuls from his sudy has shown ha he effecs of excessive real exchange rae volailiy on real labour produciviy can be negaive and fuure reforms could possibly accoun for ha. 128

142 CHAPTER 8: CONCLUSIONS AND DIRECTIONS FOR FUITURE RESEARCH 8.1 Summary Since he floaing of he Ausralian dollar in 1983, here has been a significan increase in boh nominal and real exchange rae volailiy. On an inernaional basis, here have been concerns abou he possible derimenal effecs exchange rae volailiy could have on boh rade volumes and produciviy levels. While he empirical research done in his area has been varied, mos analysis has assumed symmery in he effecs of exchange rae volailiy on rade volumes. This assumpion, as discussed in Chaper 3, is unrealisic. Whils radiional economic analysis suggess a significan causal link running from produciviy levels o exchange raes, Harris (2002) has recenly posulaed ha a reverse causaliy channel could possibly exis, whereby exchange rae depreciaions/volailiy could negaively influence produciviy levels. However, he research done in his area has only been minimal and has been limied o researchers like Harris solely in he Canadian conex. As he empirical research carried ou on he relaionship beween real exchange rae volailiy and rade/labour produciviy has been limied, his sudy ses ou o examine hese relaionships using boh linear and non linear approaches. In Chaper 2, he various heoreical models radiionally associaed wih he relaionship beween exchange rae volailiy and rade volumes are discussed. Depending on he assumpions made, boh heoreical and empirical sudies have shown ha exchange raes could have a negaive, posiive or even zero impac on rade volumes. More recen heoreical models ha incorporae he possibiliy of exporing firms responding differenly o varying degrees of volailiy are also been reviewed. However, he only empirical sudy ha has ye looked a he issue of asymmeric effecs of exchange rae volailiy has been Fang, Lai and Miller (2005). These auhors invesigae wheher expors in eigh Asian 129

143 counries reac differen o exchange rae volailiy in appreciaions and depreciaions using a bivariae GARCH-M model wih dynamic condiional correlaion. However, no empirical work has been done o esimae he overall hreshold effec of real exchange rae volailiy on Ausralian expor volumes o is major rading parners, he US and Japan. Chaper 3 discusses he possible effecs exchange rae depreciaions could have on labour produciviy hrough hree channels: he facor cos hypohesis, he innovaion gap hypohesis, and he shelering exchange rae mechanism. In addiion, he asymmeric produciviy model explaining how exchange rae volailiy could negaively impac labour produciviy is reviewed. As menioned above, only one formal modelling exercise uilising daa from 18 indusries in 14 counries, from 1970 o 1997, has been conduced. Using a panel model framework, Harris (2001) found evidence supporing he reverse causaliy link from exchange rae movemens o produciviy levels for highly open economies. The compeiiveness view of he posiive shor run effecs of exchange rae depreciaions on produciviy and longer erm negaive supply consequences of persisen exchange rae depreciaions on produciviy growh were suppored by Harris research. However, whils ineresing, he analysis is flawed in he sense ha common parameer resricions are imposed across all indusries and counries. Chaper 4 provides a succinc descripion of he variables used in he empirical analyses conduced in his hesis, including measuremen and daa sources. This is followed by a descripion of he economeric mehodologies employed in his sudy in Chaper 5. The main economeric mehodologies include: hreshold models and heir esimaion, uni roo ess, esimaion of he exchange rae volailiy measures using GARCH modelling and vecor error correcion models (VECMs) esimaion. Chaper 6 and 7 presen he resuls from he empirical analyses of he relaionship beween real exchange rae volailiy and real expor volumes/labour produciviy. The key findings from he empirical analyses conduced are: 130

144 1. The inclusion of significan hreshold effecs of exchange rae volailiy in he rade models is saisically imporan. The US and Japan rade models wihou he hreshold effecs indicae a negaive and posiive real exchange rae volailiy effec on Ausralian real expor volumes o boh he US and Japan respecively. However, when he hreshold effecs of bilaeral real exchange rae volailiy have been accouned for and incorporaed ino he esimaion of he hreshold rade models, here is an unambiguous significan overall negaive impac on Ausralian real expor volumes o he US and Japan. This implies ha he heories on rade hyseresis are more applicable and ha exporing firms do reac differenly o varying degrees of exchange rae volailiy. Specifically, for he case of he US and Japan, exporing firms in Ausralia appear o cu back on expor volumes o he US and Japan when he exchange rae volailiy exceeds a cerain hreshold level. As such, previous empirical sudies in his area have undersaed he rue underlying relaionship beween he real expor volumes and real exchange rae volailiy. 2. Granger causaliy ess conduced appear o indicae ha for he case of he US, he explanaory variables (LINCOME, LPRICE and LV) in he basic rade model do no joinly granger cause LEXP. The opposie is rue for he Japanese model. Coinegraion ess conduced for he basic and hreshold rade models sugges ha a long run equilibrium relaionship exiss for each case. Hence, he inerpreaions made on he coefficiens of he economic variables are saisically sound. 3. For our esimaed basic produciviy model, i appears ha, based on he OLS esimaes and he coinegraing equaion, all evidence are agains Harris (2002) s view. Exchange rae depreciaions/volailiy does no appear o have a negaive influence on labour produciviy levels in he Ausralian aggregae economy. Insead, he compeiiveness approach (as discussed in Chaper 3) seems o fi he resuls beer. For our model, i has been observed ha real exchange rae appreciaions and an increase in exchange rae volailiy seems o lead o an increase in labour produciviy. 131

145 4. Granger Causaliy ess find no evidence in suppor of he reverse causaliy link suggesed by Harris (2001). Insead, he resuls suppor he Balassa-Samuelson hypohesis which saes ha rends in he real exchange rae are ulimaely driven by differences in naional produciviy growh raes for he Ausralian aggregae economy. 5. The resuls also indicae a significan hreshold effec of real exchange rae volailiy on real labour produciviy in Ausralia. If only he USD/AUD real exchange rae volailiy and is hreshold effecs are considered in isolaion, hen we see ha in periods where he USD/AUD real exchange rae exceeds he hreshold value of , he overall impac of he excessive real exchange rae volailiy is negaive. Similar o he basic produciviy model, real exchange rae appreciaions (depreciaions) in he produciviy hreshold model has a negaive (posiive) impac on Ausralian real labour produciviy, supporing he compeiiveness view. However, he produciviy hreshold model does sugges ha excessive real exchange rae volailiy can negaively influence real labour produciviy levels in he conex of Ausralia. 8.2 Policy Implicaions Overall, he exisence of hreshold effecs of real exchange rae volailiy on Ausralian real expor volumes o he US and Japan sugges ha his will be an imporan issue for policy makers in Ausralia. Earlier heoreical sudies have argued ha real expor volumes could respond posiively, negaively or no respond o real exchange rae volailiy. However, wih he addiional informaion provided by he hreshold model esimaion in his sudy, we know ha an increase in real exchange rae volailiy only has a negaive impac on Ausralian expor volumes o he US and Japan if and only if he hreshold level is riggered. Alhough he incorporaion of he hreshold effecs of real exchange rae volailiy on real expor volumes ino rade policies is likely o be useful, he esimaion of he hreshold level of real exchange rae volailiy will be complicaed. On he oher hand, he basic labour produciviy model esimaed shows ha real exchange rae appreciaions (depreciaions) have significan negaive (posiive) impacs on real 132

146 labour produciviy. Therefore, i appears ha variabiliy in he real exchange rae does conribue o movemens in real labour produciviy in Ausralia. However, Granger causaliy ess conduced show no evidence of he exisence of he reverse causaliy channel in Ausralia. While he Granger causaliy ess fail o indicae he exisence of he reverse causaliy channel, he significan coefficien on real exchange rae sugges ha policies formulaed o improve real labour produciviy in Ausralia should ake ino consideraion he effecs of real exchange rae movemens and exchange rae volailiy. Moreover, his sudy shows ha when here is excessive USD/AUD real exchange rae volailiy (exceeding he hreshold level), real labour produciviy is negaively impaced. The saisically significan USD/AUD real exchange rae and volailiy variables furher confirms he need for curren policies o accoun for he plausible relaionship beween real exchange rae movemens/volailiy and real labour produciviy in Ausralia. 8.3 Limiaions and Recommendaions for Fuure Research Firsly, since he exisence of he hreshold effecs of real exchange rae volailiy on expor volumes are esablished for he case of he US and Japan, similar effecs could exis for bilaeral rade beween Ausralia and oher counries. In paricular, i appears insrucive o examine he case of China, which is rapidly growing o be one of Ausralia s op exporing markes. As his sudy has limied he empirical analyses done o expor demand funcions, i is imporan for fuure research o invesigae he plausible hreshold effecs of real exchange rae volailiy on impor volumes as well. On an inernaional basis, empirical sudies on he relaionship beween real exchange rae volailiy and rade volumes should incorporae he plausibiliy of hreshold effecs of real exchange rae volailiy also. This could possibly furher resolve he empirical ambiguiies surrounding his area of research. In addiion, since no proxy for hedging has been included in his sudy, fuure research incorporaing hedging as an explanaory variable ogeher wih he hreshold effecs of real 133

147 exchange rae volailiy may prove ineresing o see if he resuls differ from his sudy. 61 Since he R-squared for he rade models esimaed vary beween 70 o 85 per cen, his also suggess ha oher useful explanaory variables may have been omied from he analysis here. On an inernaional basis, paricularly for developing counries where hedging is no ye readily available, i migh be insrucive o apply he same model from his sudy ono hese counries. For he empirical sudy on real labour produciviy in Ausralia, i should be noed ha only he Ausralian aggregae economy has been covered in his sudy. I is insrucive o carry ou similar empirical analyses on he raded and non raded secors. This can be subsaniaed by he fac ha he non-raded secor comprises a higher percenage of real GDP han he raded secor in Ausralia. Moreover, he conribuions of he various secors of he economy o he aggregae produciviy growh are differen (Indusry Commission 1997). Hence, i is highly plausible ha one migh obain differen resuls when similar empirical analyses are carried ou in he raded and non-raded secors. Due o he curren shor and sporadic daa available for human capial, he research in his hesis has no included human capial as an explanaory variable. Fuure research in his area should incorporae his variable when daa is more readily available. Furhermore, a more exensive sudy regarding Ausralia s real labour produciviy levels relaive o he US, wih explanaory variables for boh counries included as raios should prove o be an ineresing area of fuure research. Such a relaive model canno be esimaed a he momen due o he lack of availabiliy of deailed, comparable Ausralian and US daa. Overall, as i has only been wo decades since Ausralia floaed is exchange rae in 1983, fuure research (when more daa from he pos-floa era is available) on he relaionships beween exchange rae movemens/volailiy and rade/produciviy will be useful and could possibly illusrae he full implicaions of a floaing exchange rae regime for a small, open economy like Ausralia. 61 The reason behind why no hedging variable has been included is explained in Chaper

148 CHAPTER 9: DATA APPENDICES Chaper 5 Appendix Procedure for Uni Roo Tess The procedure ha has been followed in he uni roo ess conduced in his hesis as seen in Chaper 6 and 7 are as follows. Sep 1: We sar wih he leas resricive model Y n 0 + α1 + δy 1 + β i Y i + ε i= 1 = α (9.1) In his sep, we es he null hypohesis, H : δ 0. If he null hypohesis is rejeced, we 1 = sop esing and conclude ha here is no uni roo. If he null hypohesis is no rejeced, proceed ono sep 2. Sep 2: This sep involves esing he significance of he rend erm under he null hypohesis of a uni roo. This means ha he appropriae τ saisic is used o es he significance ofα 0, given haδ = 0. If he rend is no significan, we proceed o sep 3. 1 = However, if he rend is significan, we can rees if α 0 using he sandardised normal disribuion. If he null of a uni roo canno be rejeced, hen we conclude ha he series conains a uni roo. If i is rejeced, conclude ha he series does no conain a uni roo. 1 = Sep 3: In his sep, he following equaion is esimaed: Y n 0 + δy 1 + β i Y i + ε i= 1 = α (9.2) In his sep, he null hypohesis H : δ 0 is esed. If he null hypohesis is rejeced, we 0 = sop esing and conclude ha here is no uni roo. If he null hypohesis is no rejeced, we es for he significance of he consan erm by using he appropriaeτ saisic, given 135

149 haδ = 0. If he consan erm is no significan, we proceed o sep 4. If he consan erm is significan, we es δ = 0 again, using he sandardised normal disribuion. Sep 4: In his sep, he esimaed equaion is as follows: Y n 1 + β i Y i + ε i= 1 = δ Y (9.3) We es for δ = 0 using he τ saisic. If H : δ 0 is rejeced, hen we conclude ha he 0 = series does no conain a uni roo. If i is rejeced, we conclude he series conain a uni roo. This sudy uses he auomaic lag lengh opion (based on AIC) provided by EViews for he ADF ess conduced. However, in he even when here is a discrepancy in he resuls obained by ADF ess, his sudy will conduc he ADF es in he following manner: As recommended by (Enders 2004), we sar wih a relaively long lag lengh of 10, and pare down he model by he usual -es and/or F-ess, unil he lag lenghs included are significanly differen from zero. Chaper 6 Appendix For breviy, only he firs 10 periods of correlograms will be presened. Figure 9.1: Correlogram Squared Residuals of Mean funcion (US) Mean funcion: ln R = α + ε 0 Auocorrelaion Parial Correlaion AC PAC Q-Sa Prob.*..* ***. *** *.. * *. ** * *

150 Figure 9.2: Correlogram Squared Residuals of Mean funcion (US) Mean funcion: ln R = α + βε + ε Auocorrelaion Parial Correlaion AC PAC Q-Sa Prob.*..* ***. *** *.. * *. ** * * Figure 9.3: Correlogram Squared Residuals of Mean funcion (Japan) Mean funcion: ln R = α + ε 0 Auocorrelaion Parial Correlaion AC PAC Q-Sa Prob. **. ** * * *..* *..* * * *.. * * Figure 9.4: Correlogram Squared Residuals of Mean funcion (Japan) Mean funcion: ln R = α + βε + ε Auocorrelaion Parial Correlaion AC PAC Q-Sa Prob. **. ** *..* *..* *..* * *.. * *..*

151 Figure 9.5: Time Plos for LEXP, LINCOME, PRICE, LV (US) D(LEXP) D(LINCOME) D(LPRICE) LV 138

152 Figure 9.6: Time Plos for LEXP, LINCOME, PRICE, LV (Japan) D(LEXP) D(LPRICE) D(LINCOME) LV 139

153 Tes Table 9.1: Coinegraion Tes for US Threshold Trade Model H 0 : rank = r Model 2 Model 3 Model 4 Model 5 λrace es r = ( ) r ( ) r ( ) r ( ) r ( ) r ( ) ( ) ( ) * ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) λmax es r = ( ) ( ) ( ) r ( ) ( ) ( ) r * ( ) ( ) ( ) r ( ) ( ) ( ) r ( ) ( ) ( ) r ( ) ( ) ( ) Noes: Criical values a 5% level of significance in parenheses. * Denoes non rejecion of he null hypohesis a he 5% significance level ( ) ( ) ( ) ( ) ( ) ( ) 140

154 Table 9.2: Coinegraion Tes for Japan Threshold Trade Model Tes H : rank = r Model 2 Model 3 Model 4 Model 5 0 λrace es r = ( ) r ( ) r * ( ) r ( ) r ( ) r ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) λmax es r = ( ) ( ) ( ) r ( ) ( ) ( ) r * ( ) ( ) ( ) r ( ) ( ) ( ) r ( ) ( ) ( ) r ( ) ( ) ( ) Noes: Criical values a 5% level of significance in parenheses. * Denoes non rejecion of he null hypohesis a he 5% significance level ( ) ( ) ( ) ( ) ( ) ( ) 141

155 Figure 9.7: Residual Plos US Basic Trade VECM.20 LEXP Residuals.012 LINCOME Residuals LPRICE Residuals.16 LV Residuals

156 Figure 9.8: Correlograms VECM for US basic rade model Auocorrelaions w ih 2 Sd.Err. Bounds Cor(LEXP,LEXP(-i)) Cor(LEXP,LINCOME(-i)) Cor(LEXP,LPRICE(-i)) Cor(LEXP,LV(-i)) Cor(LINCOME,LEXP(-i)) Cor(LINCOME,LINCOME(-i)) Cor(LINCOME,LPRICE(-i)) Cor(LINCOME,LV(-i)) Cor(LPRICE,LEXP(-i)) Cor(LPRICE,LINCOME(-i)) Cor(LPRICE,LPRICE(-i)) Cor(LPRICE,LV(-i)) Cor(LV,LEXP(-i)) Cor(LV,LINCOME(-i)) Cor(LV,LPRICE(-i)) Cor(LV,LV(-i))

157 Figure 9.9: Residual plos VECM for Japan basic rade model.24 LEXP Residuals.03 LINCOME Residuals LPRICE Residuals 2 LV Residuals

158 Figure 9.10: Correlograms VECM for Japan basic rade model Auocorrelaions w ih 2 Sd.Err. Bounds Cor(LEXP,LEXP(-i)) Cor(LEXP,LINCOME(-i)) Cor(LEXP,LPRICE(-i)) Cor(LEXP,LV(-i)) Cor(LINCOME,LEXP(-i)) Cor(LINCOME,LINCOME(-i)) Cor(LINCOME,LPRICE(-i)) Cor(LINCOME,LV(-i)) Cor(LPRICE,LEXP(-i)) Cor(LPRICE,LINCOME(-i)) Cor(LPRICE,LPRICE(-i)) Cor(LPRICE,LV(-i)) Cor(LV,LEXP(-i)) Cor(LV,LINCOME(-i)) Cor(LV,LPRICE(-i)) Cor(LV,LV(-i))

159 Table 9.3: Auocorrelaion LM Tes (US Basic Trade VECM) VEC Residual Serial Correlaion LM Tess H0: no serial correlaion a lag order h Lags LM-Sa Prob Probs from chi-square wih 16 df. Table 9.4: Auocorrelaion LM Tes (Japan Basic Trade VECM) VEC Residual Serial Correlaion LM Tess H0: no serial correlaion a lag order h Lags LM-Sa Prob Probs from chi-square wih 16 df. 146

160 Table 9.5: Auocorrelaion LM es (US hreshold model) VEC Residual Serial Correlaion LM Tess H0: no serial correlaion a lag order h Lags LM-Sa Prob Probs from chi-square wih 36 df. Table 9.6: Auocorrelaion LM Tes (Japan Threshold Model) VEC Residual Serial Correlaion LM Tess H0: no serial correlaion a lag order h Lags LM-Sa Prob Probs from chi-square wih 16 df. 147

161 Chaper 7 appendix.03 Figure 9.11: Time plos for firs differences of LLP, LKD, LTO, LR and LV D(LLP) D(LKD) D(LTO) D(LR) D(LV) 148

162 Figure 9.12: Threshold poin esimaion US Real Labour Produciviy Model 12.5 Threshold Tess 10.0 F-Tess LVOL 149

163 Table 9.7: Johansen Coinegraion Tes for Threshold Produciviy Model H 0 : rank = r Model 2 Model 3 Model 4 Model 5 λ race es r = ( ) r ( ) r ( ) r ( ) r ( ) r ( ) r ( ) ( ) ( ) ( ) ( ) ( ) ( ) * ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) λ max es r = ( ) r ( ) r ( ) r ( ) r ( ) r ( ) r ( ) ( ) ( ) ( ) ( ) ( ) ( ) * ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) 150

164 Figure 9.13: Residual Plos VECM for Basic Produciviy Model.010 LLP Residuals.015 LKD Residuals LTO Residuals.08 LR Residuals LV Residuals

165 -.3 Figure 9.14: Correlograms for Basic Produciviy VECM Auocorrelaions wih 2 Sd.Err. Bounds Cor(LLP,LLP(-i)) Cor(LLP,LKD(-i)) Cor(LLP,LTO(-i)) Cor(LLP,LR(-i)) Cor(LLP,LV(-i)) Cor(LKD,LLP(-i)) Cor(LKD,LKD(-i)) Cor(LKD,LTO(-i)) Cor(LKD,LR(-i)) Cor(LKD,LV(-i)) Cor(LTO,LLP(-i)) Cor(LTO,LKD(-i)) Cor(LTO,LTO(-i)) Cor(LTO,LR(-i)) Cor(LTO,LV(-i)) Cor(LR,LLP(-i)) Cor(LR,LKD(-i)) Cor(LR,LTO(-i)) Cor(LR,LR(-i)) Cor(LR,LV(-i)) Cor(LV,LLP(-i)) Cor(LV,LKD(-i)) Cor(LV,LTO(-i)) Cor(LV,LR(-i)) Cor(LV,LV(-i))

166 Table 9.8: Auocorrelaion LM Tes (Basic Produciviy Model) VEC Residual Serial Correlaion LM Tess H0: no serial correlaion a lag order h Lags LM-Sa Prob Probs from chi-square wih 25 df. 153

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