TERMS OF TRADE, OIL PRICE AND BILATERAL TRADE BALANCE OF SINGAPORE WITH MALAYSIA: SOME EMPIRICAL EVIDENCE

Transcription

1 TERMS OF TRADE, OIL PRICE AND BILATERAL TRADE BALANCE OF SINGAPORE WITH MALAYSIA: SOME EMPIRICAL EVIDENCE Wong Hock Tsen Roslinah Mahmud ABSTRACT This study examines the impacts of terms of trade and oil price on the bilateral trade balance of Singapore with Malaysia. Bilateral trade balance and terms of trade are found to be jointly determined. The Johansen (1988) cointegration method shows that in the long run, an increase in oil price will lead to an increase in terms of trade. An increase in terms of trade will lead to a decrease in bilateral trade balance. The generalized forecast error variance decomposition (Koop, Pesaran & Potter, 1996; Pesaran & Shin, 1998) shows that oil price, foreign demand, domestic demand and terms of trade are important contributors to bilateral trade balance of Singapore with Malaysia. Generally, terms of trade, domestic demand, foreign demand and oil price are important in the bilateral trade balance determination not only in the short run but also in the long run. Keywords: Terms of trade; Oil price; Bilateral trade balance; Singapore; Malaysia; Cointegration; Variance decomposition. INTRODUCTION The relationship between terms of trade and trade balance has been a perennial topic of discussions and debates since the seminal work of Harberger (1950) and Laursen and Metzler (1950). The Harberger- Laursen-Metzler (HLM) effect states that an increase (a decrease) in terms of trade, which is expressed in export price to import price, will lead to an increase (a decrease) in trade balance. This can be explained using a Keynesian framework with marginal propensity to consume less than unity. An increase in terms of trade will lead to an increase in domestic demand. This will lead to an increase in consumption

2 Wong Hock Tsen & Roslinah Mahmud but less than proportional increase in domestic demand. Thus, saving will increase and ceteris paribus, this will lead to an increase in trade balance. On the other hand, a decrease in terms of trade will lead to a decrease in saving and then this will lead to a decrease in trade balance (Otto, 2003: 157). Obstfeld (1982) shows that with perfect capital mobility, price flexibility and competitive world capital market, the relationship between terms of trade and trade balance depends upon the persistence of the terms of trade shock. When the terms of trade shock is temporary or transitory, rational agents will borrow from abroad to smooth their consumptions, which will worsen trade balance whereas when the terms of trade shock is permanent, rational agents will revise their current and expected future incomes downward without changing their savings. Hence, trade balance will be unaffected. Nonetheless, Mendoza (1995) finds that current account and terms of trade are positively correlated and this positive correlation is observed to be independence of the persistence of the terms of trade shock. Otto (2003) examines the HLM effect for a number of developing and small Organisation of Economic Cooperation and Development economies. The sample period is typically from 1960 to The results show that there is the HLM effect. An immediate effect of a positive shock to terms of trade is an increase in trade balance. However, trade balance will reduce when the terms of trade shock become more persistence. Dibooglu (2000) reports that the terms of trade shock is important in the short run whilst the demand and supply shocks dominate in the long run in explaining trade balance of the United States (US) over the period from 1973, quarter I to 1996, quarter II. Arize (1996) examines the impact of terms of trade on trade balance in sixteen countries over the period from 1973, quarter II to 1992, quarter IV. 1 The results indicate that, for a majority of the countries, there exists a positive long-run relationship between terms of trade and trade balance. This implies that improved in terms of trade will increase trade balance. Terms of trade is said to be a major source of trade balance fluctuations in a small open economy (Mendoza, 1995; Kose, 2002). This is particularly true for countries, whose export earnings are dominated by a narrow range of products and the prices of products are subjected to price fluctuations in the world market, and as a result 126

3 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence terms of trade fluctuations in those countries are observed to be more volatile (Kose, 2002). Bouakez and Kano (2008) investigate the impact of terms of trade on current account in Australia, Canada and the United Kingdom using an extended intertemporal model of current account. The results show that terms of trade movements do not affect the current account in a significant way. In two of the three cases, the model is strongly rejected by the data. The impact of terms of trade on trade balance could be on a case by case basis. It has been documented that the changes in oil price have closely related to macroeconomic performance (Barsky & Kilian, 2004). Backus and Crucini (2000) use a dynamic general equilibrium model and find that oil price accounted much of the variation in terms of trade over the last twenty five years and its quantitative role varied significantly over time. Moreover, they report that in the period, terms of trade of industrialised countries were driven mainly by the changes in oil price. Furthermore, the increased volatility in terms of trade since the end of the Bretton Woods system seemed largely due to the increased volatility in oil price rather than the increased volatility of nominal or real exchange rate (Backus & Crucini, 2000: ). Hence, oil price is an important factor in the terms of trade determination. Regnier (2007) documents that oil and energy prices have been more volatile than other commodity prices. Crude oil, refined petroleum and natural gas prices are more volatile than prices for about 95 per cent of products sold by domestic producers. Moreover, crude oil prices are more volatile than about 65 per cent of other products. Ozlale and Pekkurnaz (2010) analyse the impacts of oil prices on the current account balances for oil importing developing country, namely Turkey using a structural vector autoregressive (VAR) model. The results show that the response of current account ratio to the oil price shock increases gradually up to the first three months and then starts to decrease, which indicates a significant effect of the oil price shock in the short run. Moreover, unexpected increases in oil price will lead to the current account ratio to fall. There is not a one-for-one effect of oil prices on the current account balances. This could be due to the role of international financial integration. Oil price is found to have a significant determinant of current account balances for the 127

4 Wong Hock Tsen & Roslinah Mahmud Turkish economy even after controlling for other factors. Schubert and Turnovsky (2011) analyse the impact of oil price on a small developing economy. The impact of the oil price shock depends upon the internal production structure of an economy and the ability to access to the world financial market. Developing economies can benefit substantially from increasing the flexibility of their production technologies if they wish to avoid the costs associated with higher oil price. 2 However, the impact of oil price on terms of trade is uncertain. In the literature of the impact of oil price on economy, an increase in oil price is said to have a greater impact on an economy than a decrease in oil price (Hamilton, 2003). 3 Svensson (1982: 3-4) uses an intertemporal model to show responses of trade balance to the changes in oil price and interest rate for a small-open oil importing economy. With flexible wages and full employment, a temporary oil price increase unambiguously deteriorates trade balance through a decrease in saving whereas investment is unaffected. An expected future oil price increase, unambiguously improves the present trade balance through an increase in saving and a decrease in investment. These results show the differences in the present of saving, investment and trade balance to temporary and permanent oil price increase. 4 Kilian, Rebucci and Spatafora (2009) find that, amongst others, the transmission of the oil price shock depends upon the cause of the oil price increase. The positive oil demand shocks are associated with a statistically significant accumulation of the netforeign asset in oil exporting economies whilst the negative oil supply shocks are associated with a statistically insignificant decline in the net-foreign asset. They conclude that in predicting the effects of the oil demand and supply shocks on external balances it is necessary to consider the degree of international financial market integration and also its external portfolio configuration of an economy. Yusoff (2009) examines the impacts of real exchange rates on bilateral trade balances of Malaysia with the US, Singapore and Japan. The results suggest that bilateral trade balances, real exchange rates and domestic and foreign incomes are cointegrated. The coefficients of real exchange rates are found to be positive. The foreign and domestic incomes are important determinants of bilateral trade balances. On the other hand, this study examines the impact of terms of trade on 128

5 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence bilateral trade balance of Singapore with Malaysia using quarterly data over the period from 1979, quarter I to 2009, quarter II. It is important to examine the impact of terms of trade on bilateral trade balance as it is volatile and argued to have significant impact on trade balance (Harberger, 1950; Laursen & Metzler, 1950; Bouakez & Kano, 2008). Additionally, Singapore is a small open economy and vulnerability to the external shocks such as the terms of trade shock. This study also examines the impact of oil price on bilateral trade balance of Singapore with Malaysia. The world oil price is characterised to be highly fluctuations especially it tends to jump above the mean (Regnier, 2007). Oil is an important source of energy and the change of oil price could have a significant impact on bilateral trade balance (Kilian, Rebucci & Spatafora, 2009; Ozlale & Pekkurnaz, 2010; Schubert & Turnovsky, 2011). The Elliot, Rothenberg & Stock (1996) (ERS) and Kwiatkowski, Phillips, Schmidt & Shin (1992) (KPSS) unit root test statistics are used to examine the stationary of the data. The Johansen (1988) cointegration method is used to examine the long-run relationship among variables in the models. The error-correction model (ECM) is estimated to examine the short-run impacts of terms of trade and oil price on bilateral trade balance of Singapore with Malaysia. Finally, the generalized forecast error variance decomposition is used to examine the relationship of variables (Koop, Pesaran & Potter, 1996; Pesaran & Shin, 1998). The rest of this study is structured as follows. Section Exports of Singapore to Malaysia, Imports of Singapore from Malaysia, Terms of discusses exports of Singapore to Malaysia, imports of Singapore from Malaysia, terms of trade, and oil price. Section 3 explains the data and methodology used in this study and section 4 presents empirical results and discussions. The last section includes some concluding remarks. EXPORTS OF SINGAPORE TO MALAYSIA, IMPORTS OF SINGAPORE FROM MALAYSIA, TERMS OF TRADE AND OIL PRICE In 2009, Malaysia was the second largest trading partner of Singapore. The total trade of Singapore with Malaysia was 11.5 per cent of the overall trade of Singapore. In the same year, Malaysia was the second 129

6 Wong Hock Tsen & Roslinah Mahmud largest destination of exports of Singapore. Exports of Singapore to Malaysia accounted 11.5 per cent of the overall exports of Singapore. Moreover, Malaysia was the third largest destination of imports of Singapore. Imports of Singapore from Malaysia accounted 11.6 per cent of the overall imports of Singapore. Generally, the trade between Singapore and Malaysia was stable for the period (Table 1). Generally, the main exports of Singapore were electronic valves, petroleum products refined, parts for office and data processing machines, telecommunications equipment, data processing machines, civil engineering equipment parts, organoinorganic compounds, electrical circuit apparatus, electrical machinery not elsewhere classified and aircraft. The main imports of Singapore were electronic valves, petroleum products refines, petroleum crude, telecommunications equipment, parts for office and data processing machines, aircraft, civil engineering equipment parts, data processing machines, electrical circuit apparatus and engines and motor nonelectric (Singapore, 2010). Figure 1 displays the plot of the natural logarithms of terms of trade, oil price and bilateral trade balance over the period from 1979, quarter I to 2009, quarter II. There were relationships between oil price fluctuations and terms of trade fluctuations. Moreover, there were relationships between terms of trade fluctuations and trade balance fluctuations. Table 2 presents the summary statistics of the natural logarithms of terms of trade and oil price. The standard deviation of the natural logarithm of terms of trade was whilst the standard deviation of the natural logarithm of oil price was The natural logarithm of oil price is more volatile than the natural logarithm of terms of trade. Figure 2 displays the scatter plots of the natural logarithm of bilateral trade balance of Singapore with Malaysia with the natural logarithm of terms of trade and the natural logarithm of oil price, respectively. The scatter plot shows that there was an inverse relationship between the natural logarithm of bilateral trade balance and the natural logarithm of terms of trade. However, the relationship between the natural logarithms of bilateral trade balance and oil price was not straight forward. 130

7 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence DATA AND METHODOLOGY Bilateral trade balance (TB t ) is defined as (X t / M t ), where X t is exports of Singapore to Malaysia (millions of the US dollar) and M t is imports of Singapore from Malaysia (millions of the US dollar). Therefore, it can be expressed in the logarithm, regardless of whether exports are greater or less than imports. Moreover, it is not sensitive to the unit measurement and also it could be interpreted as nominal or real bilateral trade balance (Arize, 1996: 205). Terms of trade (TOT t ) is defined as (P x,t / P m,t ) 100, where P x,t is export price (2000 = 100), and P m,t is import price (2000 = 100). Oil price (O t ) is expressed by the world oil price (3 Spot Price Index, 2000 = 100). Domestic demand (Y s,t ) and foreign demand (Y m,t ) are expressed by Singaporean manufacturing production (2000 = 100) and Malaysian industrial production (2000 = 100) respectively. Exports of Singapore to Malaysia and imports of Singapore from Malaysia were obtained from Direction of Trade Statistics, International Monetary Fund (DOTS, IMF). The rest of the data were obtained from International Financial Statistics, International Monetary Fund (IFS, IMF). The sample period is from 1979, quarter I to 2009, quarter II. All data were seasonal adjusted or if not, they are seasonal adjusted using the Census X12 seasonal adjustment procedure. All data were transformed into the natural logarithms before estimation. The ERS and KPSS unit root test statistics are used to examine the stationary of the data. The ERS unit root test statistic is shown to have a higher power for small sample size. The KPSS test differs from the majority of the unit root tests that the hull hypothesis of a series is to be stationary against the alternative hypothesis is to be non-stationary. The KPSS statistic is based on the Lagrange Multiplier test on the residual from the ordinary least squares estimator. 5 The Johansen (1988) cointegration method is used to test the long-run relationship among variables in the model. The Johansen (1988) cointegration method proposes two likelihood ratio tests to test the number of cointegrating vectors in the system, namely the maximum eigenvalue (λ Max ) and trace (λ Trace ) statistics, which are computed, respectively as: 131

8 computed, respectively as: Wong Hock Tsen & Roslinah Mahmud Max T log( 1 r 1) λ Max = T log(1 λ r + 1 ) (1) λ Trace = T T Trace P ir1 log(1 1 λ i ) (2) i where T is the sample size, log is the natural logarithm, and λ i is the eigenvalue. The λ Max test statistic tests the null hypothesis (H 0 ) of r cointegrating vectors against the alternative hypothesis (H a ) that there where are (r + T 1) is cointegrating the sample size, vectors. log is The the λnatural logarithm, and i is the eigenvalue. T Trace test statistic tests the H 0 that has at most r cointegrating vectors, that is, the number of cointegrating test vectors statistic less tests than the or null equal hypothesis to r. The (Hcritical 0 ) of r cointegrating values for the vectors λ against the alt Max and λ Trace test statistics are tabulated in Pesaran, Shin and Smith (2000). The hypothesis distribution (H of a the ) that statistics there are depends (r + upon 1) cointegrating the number of vectors. non-stationary The Trace test statis components under the null hypothesis and whether or not a constant is the included H 0 that in the has cointegrating most r cointegrating vector. The lag vectors, length of that the is, VAR the model number of coint is determined by the Akaike Information Criterion (AIC). vectors If is there less are than two or cointegrating equal to r. The vectors critical in the values estimation, for the the Max first and Trace test s cointegrating vector shall be identified by normalising trade balance are and tabulated the second in cointegrating Pesaran, Shin vector and Smith shall (2000). be identified The distribution by normalising of the statistics terms of trade. More specifically, the first and second cointegrating upon vectors the are number specified of non-stationary respectively as components follows: under the null hypothesis and wh not a constant is included in the cointegrating vector. The lag length of the VAR m log TB t = β 10 + β 11 log TOT t + β 12 log Y s,t + β 13 log Y m,t + u 1,t (3) determined by the Akaike Information Criterion (AIC). logtot t = β 20 + β 21 logo t + u 2,t (4) where TB If there t is bilateral trade balance, TOT are two cointegrating vectors t is terms of trade, Y in the estimation, s,t is Singaporean domestic demand, Y the first coint m,t is Malaysian domestic demand, O t is oil price and u vector shall be i,t (i = 1, 2) is a disturbance term. A dummy variable, that is, one for the period identified 1997:Q3-1998:Q4 by normalising and the trade rest balance are zero is and included the second coint in the estimation as the deterministic variable to capture the influence of the Asian financial crisis, The first cointegrating vector is specified based on the model of Rose and Yellen (1989), which trade balance is estimated as a function of exchange 10 rate, domestic demand and foreign demand. The model has been used widely in the literature of the impact of exchange rate on trade balance. Conversely, the impact 132

9 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence of terms of trade instead of exchange rate on bilateral trade balance is examined in this study. The second cointegrating vector is specified based on the fact that terms of trade is strongly influenced by oil price (Backus & Crucini, 2000). The HLM effect states that an increase in terms of trade will lead to an increase in trade balance. However, an increase in terms of trade could lead to fewer exports and more imports and as a result trade balance will decrease. Therefore, the coefficient of terms of trade could be positive or negative. Exports to a foreign economy will increase as purchasing power of the trading partner rises and vice versa. Imports from a foreign economy will increase as purchasing power of domestic economy rises and vice versa. Hence the coefficient of domestic demand is expected to be negative and the coefficient of foreign demand is expected to be positive. 6 An increase in oil price would lead to a decrease or an increase in trade balance, which depends if the economy is the Engle and Granger (1987) demonstrate that cointegration implies an ECM. The net-oil Engle exporting and Granger economy (1987) demonstrate or the net-oil that cointegration importing implies economy. an ECM. Thus The the coefficient ECM in Engle this study and of oil Granger is estimated price (1987) could as: demonstrate be positive that cointegration or negative. implies an ECM. The ECM in this study is estimated as: ECM in this study is estimated as: Engle and Granger (1987) demonstrate that cointegration implies p q s an ECM. The ECM in this study is estimated as: logtb p q s t 30 31i logtott i 32i logys, t i 33i logym, t i logtbt 30 ip 0 31i logtott i iq 0 32i logys, t i is 0 33i logym, t i logtbt 30 i0 31i logtott i i0 32i logys, t i i0 33i logym, t i (5) v i0 w i0 i0 v w 34ilogOti 35ilogTBti 36EC1, t1 37EC2, t1 iv 0 34ilogOti iw 1 35ilogTBti 36EC1, t1 37EC2, t1 i0 34ilogOti i1 35ilogTBti 36EC1, t1 37EC2, t1 i0 i1 u 3,t (5) u 3,t (5) where EC u 3,t (5) 1,t-1 is the one-period lag of error-correction term from model (3), EC 2,t-1 is the one-period lag of error-correction term from model (4), where EC 1,t-1 is the one-period lag of error-correction term from model (3), EC 2,t-1 is the and where uec 3,t 1,t-1 is a is disturbance the one-period lag term. of error-correction The one-period term from lags model of error-correction (3), EC 2,t-1 is the terms where one-period ECgenerated 1,t-1 lag is of the error-correction one-period from the lag term of cointegrating error-correction from model (4), term vectors and ufrom 3,t is model a are disturbance included (3), ECterm. 2,t-1 is in The the the one-period lag of error-correction term from model (4), and u 3,t is a disturbance term. The estimation as additional explanatory variables in order to avoid the lost one-period lags of of error-correction term terms from generated model (4), from and the u 3,t is cointegrating a disturbance vectors term. The are of one-period potentially lags of relevant error-correction information. terms generated from the cointegrating vectors are one-period included The in lags the generalized estimation of error-correction as additional forecast terms explanatory error generated variance variables from the in decomposition cointegrating order to avoid vectors the is lost are used of included the estimation as additional explanatory variables in order to avoid the lost of to examine the relationship of variables (Koop, Pesaran & Potter, included potentially in relevant the estimation information. as additional explanatory variables in order to avoid the lost of 1996; potentially Pesaran relevant & information. Shin, 1998). The generalized forecast error variance decomposition potentially relevant information. determines the proportion of forecast error variance in one variable caused by the innovations in other variables. Thus, The generalized forecast error variance decomposition is used to examine the The generalized forecast error variance decomposition is used to examine the relationship The generalized of variables forecast (Koop, Pesaran error variance & 133 Potter, decomposition 1996; Pesaran is used & Shin, to examine 1998). The the relationship of variables (Koop, Pesaran & Potter, 1996; Pesaran & Shin, 1998). The relationship generalized forecast of variables error (Koop, variance Pesaran decomposition & Potter, determines 1996; Pesaran the proportion & Shin, 1998). of forecast The generalized forecast error variance decomposition determines the proportion of forecast

10 Wong Hock Tsen & Roslinah Mahmud the relative importance of a set of variables that affect a variance of another variable is determined. A key feature of the generalized forecast error variance decomposition is that it is invariant to the ordering of the variables in the VAR. Hence, it provides robust results than the orthogonalised method of Sims (1980). EMPIRICAL RESULTS AND DISCUSSIONS The ERS and KPSS unit root test statistics are reported in Table 3. The lag lengths used to estimate the ERS unit root test statistics are based on the AIC. The lag lengths used to compute the KPSS unit root test statistics are based on the Newey-West automatic bandwidth selection with the maximum lag length is set to twelve. The results of the ERS and KPSS unit root test statistics exhibit that generally all the variables are non-stationary in their levels but become stationary after taking the first differences, except cases of the ERS unit root test statistics with no trend and trend (terms of trade and oil price) and the KPSS unit root test statistic with no trend (terms of trade). For those series, it is either no strong evidence of a unit root or a stationary series. Those series could be considered as a borderline case. Thus, all the series are treated to be I(1) series. The results of the Johansen (1988) cointegration method are shown in Table 4. The results of the λ Max and λ Trace test statistics are computed with unrestricted intercepts and no trends. The result of the λ Max test statistic shows that there is one cointegrating vector. On the other hand, λ Trace test statistic shows that there are two cointegrating vectors. Hence, the evidence of two cointegrating vectors among trade balance, terms of trade, domestic demand, foreign demand and oil price can not be rejected. This study normalises trade balance as the first cointegrating vector and terms of trade as the second cointegrating vector. The results of the normalised cointegrating vectors are presented in Table 5. The likelihood ratio test statistic (LR) for the binding restrictions of the two cointegrating vectors is found to be significant. This implies that the estimation of the two cointegrating vectors is acceptable. The likelihood ratio test statistic (LL1) for the first cointegrating vector is marginally higher than the likelihood ratio test statistic (LL2) for the 134

11 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence combined of the two cointegrating vectors. However, oil price in the second cointegrating vector is found to be statistically significant. As a result, this would be a justification to estimate the two cointegrating vectors. The results of the likelihood ratio test statistic, which tests the coefficient of an explanatory variable is equal to zero, are mostly rejected at the 1 per cent level, except it is not rejected for domestic demand variable. This implies that the explanatory variables are mostly important in the bilateral trade balance determination. An increase in oil price will lead to an increase in terms of trade. An increase in terms of trade will lead to a decrease in bilateral trade balance. Thus, there is no evidence of the HLM effect in the long run. An increase in foreign demand will lead to an increase in bilateral trade balance. One explanation that domestic demand is not found to have significant impact on trade balance is that Singapore has continued to diversify its trade and the importance of trade with Malaysia remained about the same over the past years. This study uses the general-to-specific modelling strategy to find the error-correction representation. Initially, five lags of each first difference variable are used and then the dimensions of the parameter space are reduced to a final parsimonious specification by sequentially imposing statistically insignificant restrictions. The result of the ECM is given in Table 6. Figure 3 displays the plots of cumulative sum of recursive residuals (CUSUM) and cumulative sum of squares of recursive residuals (CUSUMSQ). There is no evidence of instability of the ECM. The estimation of the ECM is appropriate. On the whole, the result shows the ECM to have a high adjusted R 2, that is, The one-period lags of error-correction terms are found to be statistically significant. The results show terms of trade, domestic demand, foreign demand and oil price to have significant short-run impacts on bilateral trade balance. Moreover, the Asian financial crisis is found to affect bilateral trade of Singapore with Malaysia negatively. An increase in terms of trade will lead to an increase in bilateral trade balance. Thus there is the HLM effect in the short run. The result of the generalized forecast error variance decomposition is reported in Table 7. which is based on the 0-5, 10, 15 and 20 horizon periods. The choice of the lag length used in the estimation of the 135

12 Wong Hock Tsen & Roslinah Mahmud generalized forecast error variance decomposition is based on the Schwarz Bayesian Criterion (SBC). The result shows that oil price and foreign demand are the important contributors to the forecast error variance of bilateral trade balance whilst domestic demand and terms of trade are less important contributors. More specifically, oil price and foreign demand account for about 6 per cent and 4 per cent of the forecast error variance of bilateral trade balance, respectively whilst domestic demand and terms of trade account for about 2 per cent, respectively. A change in oil price, foreign demand, domestic demand or terms of trade is found to have an impact on bilateral trade balance. On the whole, oil price, foreign demand, domestic demand and terms of trade are important contributors to bilateral trade balance. This study finds that an increase in terms of terms of trade will lead to an increase in trade balance in the short run, which is consistent with the postulation of the HLM effect. Arize (1966) and Otto (2003), amongst others, report that improved in terms of trade would improve trade balance. However, in the long run, an increase in terms of terms of trade will lead to a decrease in trade balance. Bouakez and Kano (2008), amongst others, show that terms of trade movements do not affect the current account in a significant way. An increase in oil price will lead to an increase in terms of trade. For that reason, an increase in oil price would lead to a decrease in bilateral trade balance. Bollino (2007) reveals that higher price of oil price in the world market will lead to more imports in the US. Moreover, oil price is relatively an important contributor to the bilateral trade balance determination. Ozlale and Pekkurnaz (2010), amongst others, find the significant impact of oil price on trade balance. An increase in foreign demand will lead to an increase in bilateral trade balance. This implies that external economy is important to influence trade balance of Singapore. Bilateral trade balance of Singapore with Malaysia is adversely affected by the Asian financial crisis. Generally, terms of trade, domestic demand, foreign demand and oil price are important in the bilateral trade balance determination not only in the short run but also in the long run. Singapore is a small open economy. Its economy depends strongly upon the world economy. Domestic demand is too small to support the economy. Singapore was not exceptional hit by the contagion of 136

13 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence the Asian financial crisis despite sound economic fundamentals. The weak regional markets have significantly affect exports of Singapore as Asia accounted for a large share of exports of Singapore. The crisis has reduced the competitiveness of the Singapore economy as the Singapore dollar become relative stronger than those of currencies strongly affected by the crisis. The authority of Singapore to manage its exchange rate against a basket of currencies with the objective of achieving price stability could result exchange rate stability and this would maintain trade balance. The competitiveness of the economy could be strengthened through reduces business costs, including set-up costs and wage costs. The financial system shall be further developed to be more efficient and stronger. This would guarantee firms continue to access competitive funding and to reduce costs of doing business. Trade diversification shall be expanded particularly in emerging economies such as Cambodia, Laos, India and China. Trade diversification could reduce adverse impacts of the external shocks to trade balance. This could be achieved through providing more incentives for firms to participant in trade missions, trade fairs and trade in those emerging economies. Oil price is found to have significant impact on terms of trade, which in turn to have significant impact on bilateral trade balance. The world oil price is volatile and this would result bilateral trade balance to be volatile as well. The adverse impact of high oil price on bilateral trade balance could be reduced through the well manage of oil hedging in the futures markets. In the long run, more energy saving of technologies and solar energy shall be promoted to reduce the dependence of oil usage in the economy. As a small open economy, trade balance of Singapore is vulnerability to the external shocks such as the terms of trade shock. The well anticipated and prepared of the adverse impacts of the external shocks would reduce their impacts on bilateral trade balance. Trade diversification and the sound financial system would reduce the adverse impacts of the external shocks, which include the terms of trade shock. The flexibility of the business environments and production structures would allow the economy to adjust more easily to the external shocks which will reduce their impacts on bilateral trade balance. 137

14 Wong Hock Tsen & Roslinah Mahmud CONCLUSION This study examines the long-run and short-run impacts of terms of trade and oil price on bilateral trade balance of Singapore with Malaysia. This study finds that bilateral trade balance, terms of trade, domestic demand, foreign demand and oil price are cointegrated. More specifically, there are two cointegrating vectors. This study normalises the first cointegrating vector by bilateral trade balance and the second cointegrating vector by terms of trade. This study finds that an increase in terms of terms of trade will lead to an increase in trade balance in the short run. Thus there is the HLM effect. However in the long run, an increase in terms of terms of trade will lead to a decrease in trade balance. An increase in oil price will lead to an increase in terms of trade, which consequently will lead to a decrease in bilateral trade balance. An increase in foreign demand will lead to an increase in bilateral trade balance. Bilateral trade balance of Singapore with Malaysia is adversely affected by the Asian financial crisis. Oil price, foreign demand, domestic demand and terms of trade are important contributors to bilateral trade balance. Generally, terms of trade, domestic demand, foreign demand and oil price are important in the bilateral trade balance determination not only in the short run but also in the long run. Singapore is a small open economy. Its bilateral trade balance is vulnerable to the external shocks such as the terms of trade and oil price shocks. The exchange rate stability, the sound financial system, trade diversification and the flexibility of the business environments and production structures would reduce the gap of bilateral trade imbalance. The authors would like to thank the reviewer of the journal for the comments on an earlier version of this paper. All remaining errors are the authors. 138

15 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence NOTES 1 Arize (1996) expresses terms of trade as import price to export price. The countries examined are Canada, France, Germany, Italy, Japan, the United Kingdom, the United States, Denmark, Finland, the Netherlands, Switzerland, India, Korea, Malaysia, Mexico and Sri Lanka. 2 Bodenstein, Erceg and Guerrieri (2010) show that three reasons can account for the lack of a close relationship between oil price and the overall goods trade balance, namely the multiple shocks occur simultaneously, the oil shocks affect the non-oil balance in general equilibrium, and the different sources of oil price movements are associated with different propagation channels. 3 It has been evidenced that economic activity responses to asymmetrically to the oil price shock. An increase in oil price seems to retard aggregate economic activity by more than a fall in oil price stimulates it. Moreover, an increase in oil price deteriorates terms of trade for oil importing economies. Thus there is a wealth transfer from oil importing economies to oil exporting economies, leading to a fall of the purchasing power of firms and households in oil importing economies. If oil price is longer lasting, it can give rise to a change in the production structure and have an impact on unemployment (Lardic & Mignon, 2006: 3911). 4 See Lardic and Mignon (2006) for various transmission channels of the impact of oil price on economy. 5 Syczewska (2010) proposes that KPSS unit root test statistics shall be jointly read with other unit root test statistics. The comparison of results of the KPSS test and other unit root test statistics would improve inference quality. Examining the unit root hypothesis and stationary hypothesis together helps to distinguish the series to be stationary or to be a unit root. 6 Miljkovic, Paul and Garcia (2000: 327) report that the transitory income shocks are major reasons for the change in trade balance and negative correlation between trade balance and income is primary due to the transitory shocks. They examine five small open economies, namely Austria, France, Italy, Spain and Switzerland. The aggregate demand shocks are responsible for cyclical variations in trade balance and income and aggregate supply is responsible for the long-run economic growth. REFERENCES Arize, A.C Cointegration test of a long-run relation between the trade balance and the terms-of-trade in sixteen countries. North American Journal of Economics & Finance, 7(2), Backus, D.K. & Crucini, M.J Oil prices and the terms-of-trade. Journal of International Economics 50(1), Barsky, R. & Kilian, L Oil and the macro-economy since the 1970s. National Bureau of Economic Research Working Paper No Bodenstein, M., Erceg, C.J. & Guerrieri, L Oil shocks and external adjustment. Journal of International Economics, doi: /j.jinteco Bollino, C.A Oil prices and the U.S. trade deficit. Journal of Policy Modeling, 29(5), Bouakez, H. & Kano, T Terms of trade and current account fluctuations: The Harberger-Laursen-Metzler effect revisited. Journal of Macroeconomics, 30(1), Dibooglu, S Aggregation supply, domestic absorption and terms of trade: a structural decomposition of the U.S. trade balance. The International Trade Journal, 14(2),

16 Wong Hock Tsen & Roslinah Mahmud Elliot, G., Rothenberg, T.J. & Stock, J.H Efficient tests for an autoregressive unit root. Econometrica, 64(4), Engle, R.F. & Granger, C.W.J Co-integration and error correction: representation, estimation and testing. Econometrica, 55(2), Hamilton, J.D What is an oil shock? Journal of Econometrics, 113(2), Harberger, A.C Currency depreciation, income and the balance of trade. Journal of Political Economy, 58, Johansen, S Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control, 12(2-3), Kilian, L., Rebucci, A. & Spatafora, N Oil shocks and external balances. Journal of International Economics, 77(2), Koop, G., Pesaran, M.H., & Potter, S.M Impulse response analysis in nonlinear multivariate models. Journal of Econometrics, 74(1), Kose, M.A Explaining business cycles in small open economies: how much do world prices matter? Journal of International Economics, 56(2), Kwiatkowski, D., Phillips, P.C.B., Schmidt, P. & Shin, Y Testing the null hypothesis of stationarity against the alternative of a unit root: how sure are we that economic time series have a unit root? Journal of Econometrics, 54(1-3), Lardic, S. & Mignon, V The impact of oil price on GDP in European countries: an empirical investigation based on asymmetric cointegration. Energy Policy, 34(18), Laursen, S. & Metzler, L.A Flexible exchange rates and the theory of employment. Review of Economics and Statistics, 32(4), Mendoza, E.G The terms of trade, the real exchange rate and economic fluctuations. International Economic Review, 36(1), Miljkovic, D., Paul, R.J. & Garcia, R.J Income effects on the trade balance in small open economies. Applied Economics, 32(3), Obstfeld, M Aggregate spending and the terms of trade: is there a Laursen- Metzler effect? Quarterly Journal of Economics, 97(2), Otto, G Terms of trade shocks and the balance of trade: there is a Harberger- Laursen-Metzler effect. Journal of International Money and Finance, 22(2), Ozlale, U. & Pekkurnaz, D Oil prices and current account: a structural analysis for the Turkish economy. Energy Policy, 38(8), Pesaran, H.H., & Shin, Y Generalized impulse response analysis in linear multivariate models. Economics Letters, 58(1), Pesaran, M.H., Shin, Y. & Smith, R.J Structural analysis of vector error correction models with exogenous I(1) variables. Journal of Econometrics, 97(2), Regnier, E Oil and energy price volatility. Energy Economics, 29(3),

17 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence Rose, A.K. & Yellen, J.L Is there a J-curve? Journal of Monetary Economics, 24(1), Schubert, S.F. & Turnovsky, S.J The impact of oil prices on an oil-importing developing economy. Journal of Development Economics, 94(1), Sims, C Macroeconomics and reality. Econometrica, 48(1), Singapore (2006) Economic survey of Singapore Republic of Singapore: Ministry of Trade and Industry. Singapore (2009) Economic survey of Singapore Republic of Singapore: Ministry of Trade and Industry. Singapore (2010) Economic survey of Singapore Republic of Singapore: Ministry of Trade and Industry. Svensson, L.E.O Oil prices, welfare and the trade balance: an intertemporal model. National Bureau of Economic Research Working Paper No Syczewska, E.M Empirical power of the Kwiatkowski-Phillips-Schmidt-Shin test. Department of Applied Econometrics Working Papers, Warsaw School of Economics, Working Paper No Yusoff, M.B Bilateral trade balance, exchange rates, and income: evidence from Malaysia. Global Economy Journal, 9(4), Article

18 Wong Hock Tsen & Roslinah Mahmud TB TOT O FIGURE 1 The Plot of the Natural Logarithms of Bilateral Trade Balance IGURE 1 The Plot of of Singapore the Natural with Logarithms Malaysia, Terms of Bilateral of Trade, Trade and Oil Balance Price of Singapore with Malaysia, Terms of Trade, and Oil Price Source: IFS, IMF. Notes: TB denotes the natural logarithm of bilateral trade balance of Singapore with Malaysia (log TB t ). TOT denotes the natural logarithm of terms of trade (log TOT t ). O denotes the natural logarithm of oil price (log O t ). ource: IFS, IMF. TB and TOT TB and O otes: TB denotes the natural logarithm of bilateral trade balance of Singapore with Malaysia (log TB t ) OT denotes the natural logarithm of terms of trade (log TOT t ). O denotes the natural logarithm of oil price og O t ) FIGURE 2 The Scatter Plots of the Natural Logarithm of Bilateral Trade Balance of Singapore with Malaysia with the Natural FIGURE 2 The Scatter Plots of the Natural Logarithm of Bilateral Trade Balance of Singapore Logarithm with of Malaysia Terms with of Trade the Natural and Logarithm the Natural of Terms Logarithm of Trade of and the Natural Logarithm of Oil Price, 1979, Quarter I , Quarter II Oil Price, 1979, Quarter I , Quarter II Notes: Vertical axis denotes the natural logarithm of bilateral trade balance of Singapore with Malaysia (log TB t ) while horizontal axis denotes the natural logarithm of terms of trade (log TOT t ) or the natural Notes: logarithm Vertical of oil axis price denotes (log Othe t ). natural logarithm of bilateral trade balance of Singapore with Malaysia (log TB t ) while horizontal axis denotes the natural logarithm of terms of trade (log TOT t ) or the natural logarithm of oil price (log O t ). 142

19 24 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence CUSUM CUSUMSQ FIGURE 3 The Plots of Cumulative Sum of Recursive Residuals (CUSUM) FIGURE and Cumulative 3 The Plots Sum of Cumulative of Squares Sum of Recursive Residuals (C Residuals CUSUMSQ (CUSUMSQ) Cumulative Sum of Squares of Recursive Residuals (CU Note: The straight lines represent critical bounds at 5% significance level. TABLE 1 The Percentages of Total Trade of Singapore with Malaysia Note: The straight lines represent critical bounds at 5% significance level. (Total Trade), Exports of Singapore to Malaysia (Exports) and Imports of Singapore from Malaysia (Imports) Year Total Trade Exports Imports RE 3 The Plots of Cumulative 2009Sum of Recursive 11.5 Residuals 11.5 (CUSUM) and 11.6 Cumulative Sum of Squares of Recursive Source: Singapore Residuals (2006,(CUSUMSQ) 2009 and 2010). TABLE 2 The Summary Statistics of the Natural Logarithms of Terms of Trade (logtot 24 t ) and Oil Price (log O t ) aight lines represent critical bounds at 5% Year significance level. log TOT t log O t Maximum Minimum Mean SD Skewness Kurtosis Note: The summary statistics are calculated based on the data from IFS, IMF. 143

20 Wong Hock Tsen & Roslinah Mahmud TABLE 3 The Results of the Elliot, Rothenberg, and Stock (1996) (ERS) and Kwiatkowski, Phillips, Schmidt and Shin (1992) (KPSS) Unit Root Test Statistics ERS - No Trend KPSS - No Trend ERS - Trend KPSS - Trend log TB t (1) *(8) (1) ***(8) log TB t ***(4) (12) ***(0) (12) log TOT t (10) ***(9) (8) **(8) log TOT t (9) *(6) (9) (5) log Y s,t (5) ***(9) (4) *(8) log Y s,t ***(4) (10) ***(4) (10) log Y m,t (1) ***(9) (1) ***(9) log Y m,t ***(0) (4) ***(0) (5) log O t (4) **(9) (4) ***(9) log O t (9) (6) (9) (8) Notes: No trend denotes that the ERS or KPSS statistic is estimated based on the model including an intercept. Trend denotes that the ERS or KPSS statistic is estimated based on the model including an intercept and a time trend. Values in parentheses are the lag length used in the estimation of the ERS or KPSS unit root test statistic. *** (**, *) denotes significance at the 1% (5%, 10%) level. TABLE 4 The Results of the Likelihood Ratio Test Statistics (Johansen, 1988) λ Max Test Statistic λ Trace Test Statistic H 0 : r=0 r<=1 r<=2 r<=3 r<=4 r=0 r<=1 r<=2 r<=3 r<=4 H a : r=1 r=2 r=3 r=4 r=5 r 1 r 2 r 3 r 4 r= ** ** 53.71** c.v. (1) c.v. (2) Notes: The VAR = 5 is used. c.v. (1) and c.v. (2) denote the 5% critical value and 10% critical value, respectively. ** denotes significance at the 5% level. 144

21 Terms of Trade, Oil Price and Bilateral Trade Balance of Singapore With Malaysia: Some Empirical Evidence TABLE 5 The Results of the Normalised Cointegrating Vectors log TB t = log TOT t log Y s,t log Y m,t ( ***) (1.8713) ( ***) log TOT t = log O t ( ***) LR = ***, LL1 = , LL2 = Notes: The VAR = 5 is used in the estimation of the normalised cointegrating vectors. LR denotes the likelihood ratio test for the binding restrictions of the two cointegrating vectors. LL1 denotes the likelihood ratio test for the first cointegrating vector. LL2 denotes the likelihood ratio test for the combined of the two cointegrating vectors. Values in parentheses are the likelihood ratio test statistic. *** denotes significance at the 1% level. TABLE 6 The Result of the Error-Correction Model constant ( ***) log TOT t (2.0156**) log Y s,t (2.0909**) log Y m,t ( ***) log Y m,t (1.7472*) log O t ( **) log O t (2.0877**) log TB t ( ***) log TB t ( *) D t ( **) EC 1,t ( ***) EC 2,t (4.5154***) Diagnostic tests: Adj. R LM Reset Normal Hetero Notes: D t is the dummy variable to capture the influence of the Asian financial crisis, Adj. R 2 is the adjusted R 2. LM is the Lagrange Multiplier test of error term serial correlation. Reset is the test of functional form. Normal is the test of the normality of error term. Hetero is the test of heteroscedasticity. Values in parentheses are the t-statistic. *** (**, *) denotes significance at the 1% (5%, 10%) level. 145