Vector Time Series Model Representations and Analysis with XploRe

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Vector Time Series Model Representations and Analysis with XploRe"

Transcription

1 0-1 Vector Time Series Model Representations and Analysis with plore Julius Mungo CASE - Center for Applied Statistics and Economics Humboldt-Universität zu Berlin plore MulTi

2 Motivation 1-1 Multiple time series analysis approach involves a frame work for analyzing time series systems and the possible cross relationships among its levels. Modelling such systems entails investigating whether some variables in the system have a tendency to lead others. there is feedbacks between the variables, the question of contemporaneous movements, impulses (shocks, innovations) transfer from one time series to another plore MulTi

3 Motivation 1-2 The modelling procedure in plore uses the quantlet library MulTi to model a system of multiple time series. how plore MulTi is used to empirically investigate and modell various MTS systems. attention is on Vector Autoregressive (VAR) and the Vector Equilibrium Correction (ECM) models representations and modelling. Granger & Newbold ( 1986) plore MulTi

4 Motivation 1-3 Outline 1. Motivation 2. plore Quantlib MulTi 3. Modelling Time Dependent Factor Loadings from a DSFM for IV String Dynamics 4. Summary 5. References plore MulTi

5 plore Quantlib MulTi 2-1 MulTiplot.xpl Generates a MTS plot from a k-dimensional time series data, allowing for the series transformation, with graphics, plots and their properties to be investigated MulTiplot01.xpl plore MulTi

6 plore Quantlib MulTi 2-2 MulTifr.xpl For general analysis of the Full VAR Model; VAR order selection criteria, parameter estimation, Residual Analysis, Structural Analysis and Forecasting. MulTifr02.xpl plore MulTi

7 plore Quantlib MulTi 2-3 MulTiira.xpl For VAR impulse response analysis 1 library (" multi ") 2 x= read (" mts. dat ") 3 MulTiira (x,4,"m " " Y " " I") MulTiira01.xpl plore MulTi

8 plore Quantlib MulTi 2-4 MulTirr.xpl For Reduced Rank VAR analysis MulTiss.xpl General analysis for a Subset VAR Model MulTici.xpl General analysis for cointegration plore MulTi

9 VAR modelling with plore 3-1 VAR modelling plore specifies a k-dimensional VAR(p) model of the form Y t = υ + A 1 Y t 1 + A 2 Y t 2 +,..., +A p Y t p + ε t (1) Y t = (Y 1t,..., Y kt ) are observable vectors of k endogenous variables υ = (υ 1,..., υ k ) is a vector of intercept terms, A i are (K K) coefficient matrices ε t is a white noise with covariance matrix Σ ε > 0 plore MulTi

10 Modelling Time Dependent Factor Loadings 4-1 Modelling Time Dependent Factor Loadings from a DSFM for IV String Dynamics The DSFM is represented by Y i,j = m o ( i,j ) + l β i,l m l ( i,j ) + ɛ i,j l=1 m l are smooth basis function (l = 0, 1,..., L) i,j are two dimensional covariables β i,l are weights of m l depending on time i β i = (β i,1, β i,2,..., β i,l ) t (Fengler et al (2004)) form an observed MTS plore MulTi

11 time time time Time series plots for the beta coeff. series ( ) MTSplot.xpl Beta1 coeff. time plot number sold(thousands) Beta2 coeff. time plot number sold(thousands) Beta3 coeff. time plot number sold(thousands)*e

12 Modelling Time Dependent Factor Loadings 4-3 Min. Max. Mean Median Stdd. Skewn. Kurt. beta beta beta Table 1: Summary statistics for Beta coeff. series beta1 beta2 beta3 beta beta beta3 1 Table 2: Contemp. correlation Betasummary.xpl plore MulTi

13 Y Y Distribution: Beta Distribution: Beta Distribution: Beta *E-2 Y Y Y*E Y Modelling Time Dependent Factor Loadings 4-4 Betadensity.xpl Kernel density (Epanechnikov, h = ) and boxplot for levels plore MulTi

14 Y *E-2 Y Modelling Time Dependent Factor Loadings Y*E Figure 1: Q-Q plots of the normal against the emprical quanttiles for the Beta series plore MulTi

15 acf acf acf Sample autocorrelation function (acf) lag Sample autocorrelation function (acf) lag Sample autocorrelation function (acf) lag pacf pacf pacf Sample partial autocorrelation function (pacf) lag Sample partial autocorrelation function (pacf) lag Sample partial autocorrelation function (pacf) Modelling Time Dependent Factor Loadings 4-6 preanalysbetas.xpl lag Y plore MulTi Figure 2: ACF and PACF of levels

16 Modelling Time Dependent Factor Loadings 4-7 Testing β i levels for random walk Coeff. Test Deterministic lags testvalue asymptotic crit. values term (David & Mackinnon, (1993)) 1% 5% 10% Beta1 ADF constant Beta2 ADF constant Beta3 ADF constant Table 3: ADF-Test of unitroot for levels series plore MulTi

17 Modelling Time Dependent Factor Loadings 4-8 coeff. lag test statistic crit. values (Kwiaskowski, (1992)) 1% 5% 10% Beta1 1 const Beta2 1 const Beta3 1 const Table 4: KPSS-Test of stationarity for levels series unitrootest.xpl plore MulTi

18 Modelling Time Dependent Factor Loadings 4-9 Modelling Beta series Results: At 1% significant level, unit root exist for Beta1 and beta3 at all lags considered Beta2 indicates of some kind of misspecification Beta3 do not reject at 1% level, unit-root null hypothesis. Even at 5% or 10%, rejecting unit root will be marginal. KPSS clearly rejects its null hypothesis of stationarity around a constant plore MulTi

19 Modelling Time Dependent Factor Loadings 4-10 coeff. shift suggested test statistic crit. values (Lanne et, al(2001)) function break date (shift dummy ) 1% 5% 10% Beta Beta Beta Table 5: Unitroot-Test of stationarity for levels series in the presence of structural break We specify a stationary model with first differences and consider fitting an VAR model, t = ( Beta1, Beta2, Beta3) and determine the autoregressive order for the model plore MulTi

20 Modelling Time Dependent Factor Loadings 4-11 Beta Time Series Plot Y plore MulTi Figure 3: First difference plot of Beta series

21 Modelling Time Dependent Factor Loadings 4-12 Order Selection Criteria Final Prediction Error Akaike Information Criterion AIC = ln (n) ε = ln FPE(n) = T + kn + 1 T kn 1 ˆ ˆ (n) ε + 2nK 2 T Schwarz Information Criterion k det( ˆ ε (n)) + 2(the number of freely estimated parameters) T SIC = ln Hannan-Quinnn Information Criterion plore MulTi HQ = ln ˆ (n) ε + lnt T nk 2 ˆ (n) ε + 2ln(lnT ) T nk 2

22 Modelling Time Dependent Factor Loadings 4-13 order ln(fpe) AIC HQ SC We choose to apply the order 3 as indicated by HQ. HQ and HC have been justified as consistent, (see, Paulsen(1984) and Tsay(1984)) plore MulTi

23 Modelling Time Dependent Factor Loadings 4-14 VAR estimates (OLS) with t-values in parenthesis 2 4 Beta1t Beta2 t Beta3 t 3 5 = (3.24) 0.19(2.30) 0.06( 0.40) 0.09(6.35) 0.07( 17.17) 0.07(1.03) 0.02(2.25) 0.02(1.42) 0.26( 8.24) ( 2.40) 0.04( 0.79) 0.11(0.64) 0.03( 1.58) 0.04(7.94) 0.05( 0.66) 0.00( 0.63) 0.00(0.16) 0.06( 1.89) 0.02( 0.58) 0.13( 1.53) 0.14(+0.83) 0.00(+0.03) 0.12( 3.42) 0.02( 0.26) 0.01( 0.53) 0.02( 1.36) 0.05( 1.52) 2 4 ˆε 1,t ˆε 2,t ˆε 3,t Beta1 t 1 Beta2 t 1 Beta3 t Beta1 t 2 Beta2 t 2 Beta3 t 2 4 Beta1 t 3 Beta2 t 3 Beta3 t plore MulTi

24 Modelling Time Dependent Factor Loadings 4-15 Covariance matrix of residuals ˆΣ ε = Correlation matrix of residuals ˆ Corr(ε t ) = The correlation matrix indicates that there is some contemporaneous correlation structure in the residual vector. Not all elements of the parameter matrices are significantly different from zero. Especially the coefficients for Beta1 t 3. plore MulTi

25 Modelling Time Dependent Factor Loadings 4-16 Model Validation (i) Multivariate Portmanteau test for autocorrelation H 0 : E(ε t ε t i) = 0, i = 1,..., h H 1 : at least one autocovariance (autocorrelation) is non zero Test statistic: (Ljung & Box (1978)) h Qp = T 2 1 { T i tr C i C 1 i=1 C i = T 1 0 C i T t=i+1 ε t ε t i } C0 1 χ 2 k 2 (h p) C 0 and C i are the contemporaneous correlations and autocovariance of residuals respectively plore MulTi

26 Modelling Time Dependent Factor Loadings 4-17 (ii) Testing for ARCH effects Test for neglected conditional heteroscedasticity (ARCH) based on fitting ARCH(q) model to the estimated residuals. ˆε 2 t = β 0 + β 1ˆε 2 t β q ˆε 2 t q + error t H 0 : β 1 = = β q = 0, (no ARCH effects) H 1 : β 1 0 or... or β q 0 plore MulTi

27 Modelling Time Dependent Factor Loadings 4-18 Lagrange Multiplier (LM) statistic: (see, Engle (1982)) The R 2 form, test statistic: ARCH LM = 1 2 ˆε t ˆε χ 2 q T R 2 χ 2 q R 2 is the squared multiple R 2 value of the regression of ˆε 2 t on an intercept and q lagged values of ˆε 2 t ARCHtest.xpl plore MulTi

28 Modelling Time Dependent Factor Loadings 4-19 (iii) Testing for Nonnormality H 0 : E(µ s t) 3 = 0 & E(µ s t) 3 = 0 H 1 : E(µ s t) 3 0 & E(µ s t) 3 0 Test statistic: (Jarque and Bera (1987)) JB = T 6 ( T ) 2 ( T 1 (ˆµ s t) 3 + T T T 1 (ˆµ s 24 t) 4 3 t=1 The test displays the χ 2 -statistics associated with the skewness and kurtosis of the standardized residuals for testing nonnormality. t=1 ) 2 plore MulTi

29 Modelling Time Dependent Factor Loadings 4-20 Test Q3 JB 3 MARCH LM (3) Test statistic p-value Table 6: Diagnostic tests for AR(3) models The tests hypothesis is rejected for p-values smaller than Results show some autocorrelation in the residuals and the presence of heteroscedastic effects in the conditional variance. We therefore maintain that there is some ARCH effects in model residuals. plore MulTi

30 Summary 5-21 Summary Testing for ARCH effects reveal neglected conditional heteroscedasticity. This gives an indication of fitting an ARCH or ARCH type model. Observing that not all elements of the estimated VAR parameter matrices are significantly different from zero, we could choose a subset VAR model where single elements of the estimated coefficient matrices are restricted to zero. plore MulTi

31 References 6-22 References G.C Reinsel Elements of Multivariate Time Series Anylysis. Springer Verlag, New York, W. Härdle, Z. Hlávka and S. Klinke plore Application Guide Springer-Verlag, Heidelberg, H. Lütkepohl Introduction to Multiple Time Series Analysis. Springer Verlag,1993. K. Patterson An Introduction to Applied Econometrics a time series approach. Macmillan Press Ltd, plore MulTi

Chapter 6: Multivariate Cointegration Analysis

Chapter 6: Multivariate Cointegration Analysis Chapter 6: Multivariate Cointegration Analysis 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie VI. Multivariate Cointegration

More information

Chapter 5: Bivariate Cointegration Analysis

Chapter 5: Bivariate Cointegration Analysis Chapter 5: Bivariate Cointegration Analysis 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie V. Bivariate Cointegration Analysis...

More information

Chapter 5. Analysis of Multiple Time Series. 5.1 Vector Autoregressions

Chapter 5. Analysis of Multiple Time Series. 5.1 Vector Autoregressions Chapter 5 Analysis of Multiple Time Series Note: The primary references for these notes are chapters 5 and 6 in Enders (2004). An alternative, but more technical treatment can be found in chapters 10-11

More information

Performing Unit Root Tests in EViews. Unit Root Testing

Performing Unit Root Tests in EViews. Unit Root Testing Página 1 de 12 Unit Root Testing The theory behind ARMA estimation is based on stationary time series. A series is said to be (weakly or covariance) stationary if the mean and autocovariances of the series

More information

Testing The Quantity Theory of Money in Greece: A Note

Testing The Quantity Theory of Money in Greece: A Note ERC Working Paper in Economic 03/10 November 2003 Testing The Quantity Theory of Money in Greece: A Note Erdal Özmen Department of Economics Middle East Technical University Ankara 06531, Turkey ozmen@metu.edu.tr

More information

THE IMPACT OF EXCHANGE RATE VOLATILITY ON BRAZILIAN MANUFACTURED EXPORTS

THE IMPACT OF EXCHANGE RATE VOLATILITY ON BRAZILIAN MANUFACTURED EXPORTS THE IMPACT OF EXCHANGE RATE VOLATILITY ON BRAZILIAN MANUFACTURED EXPORTS ANTONIO AGUIRRE UFMG / Department of Economics CEPE (Centre for Research in International Economics) Rua Curitiba, 832 Belo Horizonte

More information

Implied volatility transmissions between Thai and selected advanced stock markets

Implied volatility transmissions between Thai and selected advanced stock markets MPRA Munich Personal RePEc Archive Implied volatility transmissions between Thai and selected advanced stock markets Supachok Thakolsri and Yuthana Sethapramote and Komain Jiranyakul Public Enterprise

More information

Department of Economics

Department of Economics Department of Economics Working Paper Do Stock Market Risk Premium Respond to Consumer Confidence? By Abdur Chowdhury Working Paper 2011 06 College of Business Administration Do Stock Market Risk Premium

More information

A Trading Strategy Based on the Lead-Lag Relationship of Spot and Futures Prices of the S&P 500

A Trading Strategy Based on the Lead-Lag Relationship of Spot and Futures Prices of the S&P 500 A Trading Strategy Based on the Lead-Lag Relationship of Spot and Futures Prices of the S&P 500 FE8827 Quantitative Trading Strategies 2010/11 Mini-Term 5 Nanyang Technological University Submitted By:

More information

Chapter 4: Vector Autoregressive Models

Chapter 4: Vector Autoregressive Models Chapter 4: Vector Autoregressive Models 1 Contents: Lehrstuhl für Department Empirische of Wirtschaftsforschung Empirical Research and und Econometrics Ökonometrie IV.1 Vector Autoregressive Models (VAR)...

More information

Time Series Analysis

Time Series Analysis Time Series Analysis Identifying possible ARIMA models Andrés M. Alonso Carolina García-Martos Universidad Carlos III de Madrid Universidad Politécnica de Madrid June July, 2012 Alonso and García-Martos

More information

TEMPORAL CAUSAL RELATIONSHIP BETWEEN STOCK MARKET CAPITALIZATION, TRADE OPENNESS AND REAL GDP: EVIDENCE FROM THAILAND

TEMPORAL CAUSAL RELATIONSHIP BETWEEN STOCK MARKET CAPITALIZATION, TRADE OPENNESS AND REAL GDP: EVIDENCE FROM THAILAND I J A B E R, Vol. 13, No. 4, (2015): 1525-1534 TEMPORAL CAUSAL RELATIONSHIP BETWEEN STOCK MARKET CAPITALIZATION, TRADE OPENNESS AND REAL GDP: EVIDENCE FROM THAILAND Komain Jiranyakul * Abstract: This study

More information

Import and Economic Growth in Turkey: Evidence from Multivariate VAR Analysis

Import and Economic Growth in Turkey: Evidence from Multivariate VAR Analysis Journal of Economics and Business Vol. XI 2008, No 1 & No 2 Import and Economic Growth in Turkey: Evidence from Multivariate VAR Analysis Ahmet Uğur, Inonu University Abstract This study made an attempt

More information

Examining the Relationship between ETFS and Their Underlying Assets in Indian Capital Market

Examining the Relationship between ETFS and Their Underlying Assets in Indian Capital Market 2012 2nd International Conference on Computer and Software Modeling (ICCSM 2012) IPCSIT vol. 54 (2012) (2012) IACSIT Press, Singapore DOI: 10.7763/IPCSIT.2012.V54.20 Examining the Relationship between

More information

Granger Causality between Government Revenues and Expenditures in Korea

Granger Causality between Government Revenues and Expenditures in Korea Volume 23, Number 1, June 1998 Granger Causality between Government Revenues and Expenditures in Korea Wan Kyu Park ** 2 This paper investigates the Granger causal relationship between government revenues

More information

Note 2 to Computer class: Standard mis-specification tests

Note 2 to Computer class: Standard mis-specification tests Note 2 to Computer class: Standard mis-specification tests Ragnar Nymoen September 2, 2013 1 Why mis-specification testing of econometric models? As econometricians we must relate to the fact that the

More information

Some useful concepts in univariate time series analysis

Some useful concepts in univariate time series analysis Some useful concepts in univariate time series analysis Autoregressive moving average models Autocorrelation functions Model Estimation Diagnostic measure Model selection Forecasting Assumptions: 1. Non-seasonal

More information

ENDOGENOUS GROWTH MODELS AND STOCK MARKET DEVELOPMENT: EVIDENCE FROM FOUR COUNTRIES

ENDOGENOUS GROWTH MODELS AND STOCK MARKET DEVELOPMENT: EVIDENCE FROM FOUR COUNTRIES ENDOGENOUS GROWTH MODELS AND STOCK MARKET DEVELOPMENT: EVIDENCE FROM FOUR COUNTRIES Guglielmo Maria Caporale, South Bank University London Peter G. A Howells, University of East London Alaa M. Soliman,

More information

Univariate and Multivariate Methods PEARSON. Addison Wesley

Univariate and Multivariate Methods PEARSON. Addison Wesley Time Series Analysis Univariate and Multivariate Methods SECOND EDITION William W. S. Wei Department of Statistics The Fox School of Business and Management Temple University PEARSON Addison Wesley Boston

More information

Software Review: ITSM 2000 Professional Version 6.0.

Software Review: ITSM 2000 Professional Version 6.0. Lee, J. & Strazicich, M.C. (2002). Software Review: ITSM 2000 Professional Version 6.0. International Journal of Forecasting, 18(3): 455-459 (June 2002). Published by Elsevier (ISSN: 0169-2070). http://0-

More information

Cointegration And Causality Analysis of Government Expenditure And Economic Growth In Nigeria

Cointegration And Causality Analysis of Government Expenditure And Economic Growth In Nigeria Cointegration And Causality Analysis of Government Expenditure And Economic Growth In Nigeria Chiawa, M. M, Torruam, J. T, Abur, C. C Abstract:- The study investigates government expenditure and economic

More information

Analysis and Computation for Finance Time Series - An Introduction

Analysis and Computation for Finance Time Series - An Introduction ECMM703 Analysis and Computation for Finance Time Series - An Introduction Alejandra González Harrison 161 Email: mag208@exeter.ac.uk Time Series - An Introduction A time series is a sequence of observations

More information

Forecasting the US Dollar / Euro Exchange rate Using ARMA Models

Forecasting the US Dollar / Euro Exchange rate Using ARMA Models Forecasting the US Dollar / Euro Exchange rate Using ARMA Models LIUWEI (9906360) - 1 - ABSTRACT...3 1. INTRODUCTION...4 2. DATA ANALYSIS...5 2.1 Stationary estimation...5 2.2 Dickey-Fuller Test...6 3.

More information

The Relationship Between Crude Oil and Natural Gas Prices

The Relationship Between Crude Oil and Natural Gas Prices The Relationship Between Crude Oil and Natural Gas Prices by Jose A. Villar Natural Gas Division Energy Information Administration and Frederick L. Joutz Department of Economics The George Washington University

More information

3.1 Stationary Processes and Mean Reversion

3.1 Stationary Processes and Mean Reversion 3. Univariate Time Series Models 3.1 Stationary Processes and Mean Reversion Definition 3.1: A time series y t, t = 1,..., T is called (covariance) stationary if (1) E[y t ] = µ, for all t Cov[y t, y t

More information

Business Cycles and Natural Gas Prices

Business Cycles and Natural Gas Prices Department of Economics Discussion Paper 2004-19 Business Cycles and Natural Gas Prices Apostolos Serletis Department of Economics University of Calgary Canada and Asghar Shahmoradi Department of Economics

More information

Time Series Analysis

Time Series Analysis Time Series 1 April 9, 2013 Time Series Analysis This chapter presents an introduction to the branch of statistics known as time series analysis. Often the data we collect in environmental studies is collected

More information

Business cycles and natural gas prices

Business cycles and natural gas prices Business cycles and natural gas prices Apostolos Serletis and Asghar Shahmoradi Abstract This paper investigates the basic stylised facts of natural gas price movements using data for the period that natural

More information

ITSM-R Reference Manual

ITSM-R Reference Manual ITSM-R Reference Manual George Weigt June 5, 2015 1 Contents 1 Introduction 3 1.1 Time series analysis in a nutshell............................... 3 1.2 White Noise Variance.....................................

More information

Testing for Granger causality between stock prices and economic growth

Testing for Granger causality between stock prices and economic growth MPRA Munich Personal RePEc Archive Testing for Granger causality between stock prices and economic growth Pasquale Foresti 2006 Online at http://mpra.ub.uni-muenchen.de/2962/ MPRA Paper No. 2962, posted

More information

Studying Achievement

Studying Achievement Journal of Business and Economics, ISSN 2155-7950, USA November 2014, Volume 5, No. 11, pp. 2052-2056 DOI: 10.15341/jbe(2155-7950)/11.05.2014/009 Academic Star Publishing Company, 2014 http://www.academicstar.us

More information

Forecasting Stock Market Series. with ARIMA Model

Forecasting Stock Market Series. with ARIMA Model Journal of Statistical and Econometric Methods, vol.3, no.3, 2014, 65-77 ISSN: 2241-0384 (print), 2241-0376 (online) Scienpress Ltd, 2014 Forecasting Stock Market Series with ARIMA Model Fatai Adewole

More information

Sales forecasting # 2

Sales forecasting # 2 Sales forecasting # 2 Arthur Charpentier arthur.charpentier@univ-rennes1.fr 1 Agenda Qualitative and quantitative methods, a very general introduction Series decomposition Short versus long term forecasting

More information

Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model

Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model Tropical Agricultural Research Vol. 24 (): 2-3 (22) Forecasting of Paddy Production in Sri Lanka: A Time Series Analysis using ARIMA Model V. Sivapathasundaram * and C. Bogahawatte Postgraduate Institute

More information

Advanced Forecasting Techniques and Models: ARIMA

Advanced Forecasting Techniques and Models: ARIMA Advanced Forecasting Techniques and Models: ARIMA Short Examples Series using Risk Simulator For more information please visit: www.realoptionsvaluation.com or contact us at: admin@realoptionsvaluation.com

More information

Unit root properties of natural gas spot and futures prices: The relevance of heteroskedasticity in high frequency data

Unit root properties of natural gas spot and futures prices: The relevance of heteroskedasticity in high frequency data DEPARTMENT OF ECONOMICS ISSN 1441-5429 DISCUSSION PAPER 20/14 Unit root properties of natural gas spot and futures prices: The relevance of heteroskedasticity in high frequency data Vinod Mishra and Russell

More information

Explaining Cointegration Analysis: Part II

Explaining Cointegration Analysis: Part II Explaining Cointegration Analysis: Part II David F. Hendry and Katarina Juselius Nuffield College, Oxford, OX1 1NF. Department of Economics, University of Copenhagen, Denmark Abstract We describe the concept

More information

Provided in Cooperation with: Darmstadt University of Technology, Department of Law and Economics

Provided in Cooperation with: Darmstadt University of Technology, Department of Law and Economics econstor www.econstor.eu Der Open-Access-Publikationsserver der ZBW Leibniz-Informationszentrum Wirtschaft The Open Access Publication Server of the ZBW Leibniz Information Centre for Economics Röthig,

More information

The Long-Run Relation Between The Personal Savings Rate And Consumer Sentiment

The Long-Run Relation Between The Personal Savings Rate And Consumer Sentiment The Long-Run Relation Between The Personal Savings Rate And Consumer Sentiment Bradley T. Ewing 1 and James E. Payne 2 This study examined the long run relationship between the personal savings rate and

More information

TIME SERIES ANALYSIS

TIME SERIES ANALYSIS TIME SERIES ANALYSIS L.M. BHAR AND V.K.SHARMA Indian Agricultural Statistics Research Institute Library Avenue, New Delhi-0 02 lmb@iasri.res.in. Introduction Time series (TS) data refers to observations

More information

Preholiday Returns and Volatility in Thai stock market

Preholiday Returns and Volatility in Thai stock market Preholiday Returns and Volatility in Thai stock market Nopphon Tangjitprom Martin de Tours School of Management and Economics, Assumption University Bangkok, Thailand Tel: (66) 8-5815-6177 Email: tnopphon@gmail.com

More information

Threshold Autoregressive Models in Finance: A Comparative Approach

Threshold Autoregressive Models in Finance: A Comparative Approach University of Wollongong Research Online Applied Statistics Education and Research Collaboration (ASEARC) - Conference Papers Faculty of Informatics 2011 Threshold Autoregressive Models in Finance: A Comparative

More information

Time Series Analysis

Time Series Analysis JUNE 2012 Time Series Analysis CONTENT A time series is a chronological sequence of observations on a particular variable. Usually the observations are taken at regular intervals (days, months, years),

More information

DETERMINANTS OF THE CHILEAN SOVEREIGN SPREAD: IS IT PURELY FUNDAMENTALS?

DETERMINANTS OF THE CHILEAN SOVEREIGN SPREAD: IS IT PURELY FUNDAMENTALS? BANCO CENTRAL DE CHILE DETERMINANTS OF THE CHILEAN SOVEREIGN SPREAD: IS IT PURELY FUNDAMENTALS? ALVARO ROJAS O. * FELIPE JAQUE S. ABSTRACT In recent years, the Chilean economy has been widely recognized

More information

TOURISM AS A LONG-RUN ECONOMIC GROWTH FACTOR: AN EMPIRICAL INVESTIGATION FOR GREECE USING CAUSALITY ANALYSIS. Nikolaos Dritsakis

TOURISM AS A LONG-RUN ECONOMIC GROWTH FACTOR: AN EMPIRICAL INVESTIGATION FOR GREECE USING CAUSALITY ANALYSIS. Nikolaos Dritsakis TOURISM AS A LONG-RUN ECONOMIC GROWTH FACTOR: AN EMPIRICAL INVESTIGATION FOR GREECE USING CAUSALITY ANALYSIS Nikolaos Dritsakis Department of Applied Informatics University of Macedonia Economics and Social

More information

FDI and Economic Growth Relationship: An Empirical Study on Malaysia

FDI and Economic Growth Relationship: An Empirical Study on Malaysia International Business Research April, 2008 FDI and Economic Growth Relationship: An Empirical Study on Malaysia Har Wai Mun Faculty of Accountancy and Management Universiti Tunku Abdul Rahman Bander Sungai

More information

Unit Labor Costs and the Price Level

Unit Labor Costs and the Price Level Unit Labor Costs and the Price Level Yash P. Mehra A popular theoretical model of the inflation process is the expectationsaugmented Phillips-curve model. According to this model, prices are set as markup

More information

ADVANCED FORECASTING MODELS USING SAS SOFTWARE

ADVANCED FORECASTING MODELS USING SAS SOFTWARE ADVANCED FORECASTING MODELS USING SAS SOFTWARE Girish Kumar Jha IARI, Pusa, New Delhi 110 012 gjha_eco@iari.res.in 1. Transfer Function Model Univariate ARIMA models are useful for analysis and forecasting

More information

IS THERE A LONG-RUN RELATIONSHIP

IS THERE A LONG-RUN RELATIONSHIP 7. IS THERE A LONG-RUN RELATIONSHIP BETWEEN TAXATION AND GROWTH: THE CASE OF TURKEY Salih Turan KATIRCIOGLU Abstract This paper empirically investigates long-run equilibrium relationship between economic

More information

The VAR models discussed so fare are appropriate for modeling I(0) data, like asset returns or growth rates of macroeconomic time series.

The VAR models discussed so fare are appropriate for modeling I(0) data, like asset returns or growth rates of macroeconomic time series. Cointegration The VAR models discussed so fare are appropriate for modeling I(0) data, like asset returns or growth rates of macroeconomic time series. Economic theory, however, often implies equilibrium

More information

Asian Economic and Financial Review THE EFFECT OF INTEREST RATE, INFLATION RATE, GDP, ON REAL ECONOMIC GROWTH RATE IN JORDAN. Abdul Aziz Farid Saymeh

Asian Economic and Financial Review THE EFFECT OF INTEREST RATE, INFLATION RATE, GDP, ON REAL ECONOMIC GROWTH RATE IN JORDAN. Abdul Aziz Farid Saymeh Asian Economic and Financial Review journal homepage: http://aessweb.com/journal-detail.php?id=52 THE EFFECT OF INTEREST RATE, INFLATION RATE, GDP, ON REAL ECONOMIC GROWTH RATE IN JORDAN Abdul Aziz Farid

More information

Using graphical modelling in official statistics

Using graphical modelling in official statistics Quaderni di Statistica Vol. 6, 2004 Using graphical modelling in official statistics Richard N. Penny Fidelio Consultancy E-mail: cpenny@xtra.co.nz Marco Reale Mathematics and Statistics Department, University

More information

Is the Basis of the Stock Index Futures Markets Nonlinear?

Is the Basis of the Stock Index Futures Markets Nonlinear? University of Wollongong Research Online Applied Statistics Education and Research Collaboration (ASEARC) - Conference Papers Faculty of Engineering and Information Sciences 2011 Is the Basis of the Stock

More information

TURUN YLIOPISTO UNIVERSITY OF TURKU TALOUSTIEDE DEPARTMENT OF ECONOMICS RESEARCH REPORTS. A nonlinear moving average test as a robust test for ARCH

TURUN YLIOPISTO UNIVERSITY OF TURKU TALOUSTIEDE DEPARTMENT OF ECONOMICS RESEARCH REPORTS. A nonlinear moving average test as a robust test for ARCH TURUN YLIOPISTO UNIVERSITY OF TURKU TALOUSTIEDE DEPARTMENT OF ECONOMICS RESEARCH REPORTS ISSN 0786 656 ISBN 951 9 1450 6 A nonlinear moving average test as a robust test for ARCH Jussi Tolvi No 81 May

More information

Modeling and forecasting regional GDP in Sweden. using autoregressive models

Modeling and forecasting regional GDP in Sweden. using autoregressive models MASTER THESIS IN MICRODATA ANALYSIS Modeling and forecasting regional GDP in Sweden using autoregressive models Author: Haonan Zhang Supervisor: Niklas Rudholm 2013 Business Intelligence Program School

More information

Government bond market linkages: evidence from Europe

Government bond market linkages: evidence from Europe Applied Financial Economics, 2005, 15, 599 610 Government bond market linkages: evidence from Europe Jian Yang Department of Accounting, Finance & MIS, Prairie View A&M University, Prairie View, TX 77446,

More information

SYSTEMS OF REGRESSION EQUATIONS

SYSTEMS OF REGRESSION EQUATIONS SYSTEMS OF REGRESSION EQUATIONS 1. MULTIPLE EQUATIONS y nt = x nt n + u nt, n = 1,...,N, t = 1,...,T, x nt is 1 k, and n is k 1. This is a version of the standard regression model where the observations

More information

Empirical Properties of the Indonesian Rupiah: Testing for Structural Breaks, Unit Roots, and White Noise

Empirical Properties of the Indonesian Rupiah: Testing for Structural Breaks, Unit Roots, and White Noise Volume 24, Number 2, December 1999 Empirical Properties of the Indonesian Rupiah: Testing for Structural Breaks, Unit Roots, and White Noise Reza Yamora Siregar * 1 This paper shows that the real exchange

More information

Internet Appendix to Stock Market Liquidity and the Business Cycle

Internet Appendix to Stock Market Liquidity and the Business Cycle Internet Appendix to Stock Market Liquidity and the Business Cycle Randi Næs, Johannes A. Skjeltorp and Bernt Arne Ødegaard This Internet appendix contains additional material to the paper Stock Market

More information

Rob J Hyndman. Forecasting using. 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1

Rob J Hyndman. Forecasting using. 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1 Rob J Hyndman Forecasting using 11. Dynamic regression OTexts.com/fpp/9/1/ Forecasting using R 1 Outline 1 Regression with ARIMA errors 2 Example: Japanese cars 3 Using Fourier terms for seasonality 4

More information

Predictability of Non-Linear Trading Rules in the US Stock Market Chong & Lam 2010

Predictability of Non-Linear Trading Rules in the US Stock Market Chong & Lam 2010 Department of Mathematics QF505 Topics in quantitative finance Group Project Report Predictability of on-linear Trading Rules in the US Stock Market Chong & Lam 010 ame: Liu Min Qi Yichen Zhang Fengtian

More information

University. Georgia State University. The Viability of Fiscal Policy in South Korea, Taiwan, and Thailand. International Studies Program

University. Georgia State University. The Viability of Fiscal Policy in South Korea, Taiwan, and Thailand. International Studies Program University International Studies Program Working Paper 02-09 March 2002 The Viability of Fiscal Policy in South Korea, Taiwan, and Thailand Tsangyao Chang WenRong Liu Henry Thompson Georgia State University

More information

Moody Oil What is Driving the Crude Oil Price?

Moody Oil What is Driving the Crude Oil Price? Moody Oil What is Driving the Crude Oil Price? Abstract The unparalleled surge of the crude oil price after 2003 has triggered a heated scientific and public debate about its ultimate causes. Unexpected

More information

Keywords: Baltic stock markets, unit root, Engle-Granger approach, Johansen cointegration test, causality, impulse response, variance decomposition.

Keywords: Baltic stock markets, unit root, Engle-Granger approach, Johansen cointegration test, causality, impulse response, variance decomposition. Department of Economics Master thesis January 28 Dynamic linkages between Baltic and International stock markets Author: Julija Moroza Supervisor: Hossein Asgharian Abstract 1 The fact is that high integration

More information

Financial Integration of Stock Markets in the Gulf: A Multivariate Cointegration Analysis

Financial Integration of Stock Markets in the Gulf: A Multivariate Cointegration Analysis INTERNATIONAL JOURNAL OF BUSINESS, 8(3), 2003 ISSN:1083-4346 Financial Integration of Stock Markets in the Gulf: A Multivariate Cointegration Analysis Aqil Mohd. Hadi Hassan Department of Economics, College

More information

TIME SERIES ANALYSIS

TIME SERIES ANALYSIS TIME SERIES ANALYSIS Ramasubramanian V. I.A.S.R.I., Library Avenue, New Delhi- 110 012 ram_stat@yahoo.co.in 1. Introduction A Time Series (TS) is a sequence of observations ordered in time. Mostly these

More information

A cointegration and causality analysis of Scandinavian stock markets

A cointegration and causality analysis of Scandinavian stock markets A cointegration and causality analysis of Scandinavian stock markets Sanda Hubana Trondheim, May 2013 Master s thesis in Financial Economics Norwegian University of Science and Technology Faculty of Social

More information

Department of Economics

Department of Economics Department of Economics On Testing for Diagonality of Large Dimensional Covariance Matrices George Kapetanios Working Paper No. 526 October 2004 ISSN 1473-0278 On Testing for Diagonality of Large Dimensional

More information

Lecture 6: Poisson regression

Lecture 6: Poisson regression Lecture 6: Poisson regression Claudia Czado TU München c (Claudia Czado, TU Munich) ZFS/IMS Göttingen 2004 0 Overview Introduction EDA for Poisson regression Estimation and testing in Poisson regression

More information

The Relationship between Life Insurance and Economic Growth: Evidence from India

The Relationship between Life Insurance and Economic Growth: Evidence from India Global Journal of Management and Business Studies. ISSN 2248-9878 Volume 3, Number 4 (2013), pp. 413-422 Research India Publications http://www.ripublication.com/gjmbs.htm The Relationship between Life

More information

COINTEGRATION AND CAUSAL RELATIONSHIP AMONG CRUDE PRICE, DOMESTIC GOLD PRICE AND FINANCIAL VARIABLES- AN EVIDENCE OF BSE AND NSE *

COINTEGRATION AND CAUSAL RELATIONSHIP AMONG CRUDE PRICE, DOMESTIC GOLD PRICE AND FINANCIAL VARIABLES- AN EVIDENCE OF BSE AND NSE * Journal of Contemporary Issues in Business Research ISSN 2305-8277 (Online), 2013, Vol. 2, No. 1, 1-10. Copyright of the Academic Journals JCIBR All rights reserved. COINTEGRATION AND CAUSAL RELATIONSHIP

More information

Least Squares Estimation

Least Squares Estimation Least Squares Estimation SARA A VAN DE GEER Volume 2, pp 1041 1045 in Encyclopedia of Statistics in Behavioral Science ISBN-13: 978-0-470-86080-9 ISBN-10: 0-470-86080-4 Editors Brian S Everitt & David

More information

PITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU

PITFALLS IN TIME SERIES ANALYSIS. Cliff Hurvich Stern School, NYU PITFALLS IN TIME SERIES ANALYSIS Cliff Hurvich Stern School, NYU The t -Test If x 1,..., x n are independent and identically distributed with mean 0, and n is not too small, then t = x 0 s n has a standard

More information

The price-volume relationship of the Malaysian Stock Index futures market

The price-volume relationship of the Malaysian Stock Index futures market The price-volume relationship of the Malaysian Stock Index futures market ABSTRACT Carl B. McGowan, Jr. Norfolk State University Junaina Muhammad University Putra Malaysia The objective of this study is

More information

Time Series Analysis and Forecasting

Time Series Analysis and Forecasting Time Series Analysis and Forecasting Math 667 Al Nosedal Department of Mathematics Indiana University of Pennsylvania Time Series Analysis and Forecasting p. 1/11 Introduction Many decision-making applications

More information

MGT 267 PROJECT. Forecasting the United States Retail Sales of the Pharmacies and Drug Stores. Done by: Shunwei Wang & Mohammad Zainal

MGT 267 PROJECT. Forecasting the United States Retail Sales of the Pharmacies and Drug Stores. Done by: Shunwei Wang & Mohammad Zainal MGT 267 PROJECT Forecasting the United States Retail Sales of the Pharmacies and Drug Stores Done by: Shunwei Wang & Mohammad Zainal Dec. 2002 The retail sale (Million) ABSTRACT The present study aims

More information

COURSES: 1. Short Course in Econometrics for the Practitioner (P000500) 2. Short Course in Econometric Analysis of Cointegration (P000537)

COURSES: 1. Short Course in Econometrics for the Practitioner (P000500) 2. Short Course in Econometric Analysis of Cointegration (P000537) Get the latest knowledge from leading global experts. Financial Science Economics Economics Short Courses Presented by the Department of Economics, University of Pretoria WITH 2015 DATES www.ce.up.ac.za

More information

Non-Stationary Time Series andunitroottests

Non-Stationary Time Series andunitroottests Econometrics 2 Fall 2005 Non-Stationary Time Series andunitroottests Heino Bohn Nielsen 1of25 Introduction Many economic time series are trending. Important to distinguish between two important cases:

More information

Forecasting areas and production of rice in India using ARIMA model

Forecasting areas and production of rice in India using ARIMA model International Journal of Farm Sciences 4(1) :99-106, 2014 Forecasting areas and production of rice in India using ARIMA model K PRABAKARAN and C SIVAPRAGASAM* Agricultural College and Research Institute,

More information

Time Series Analysis

Time Series Analysis Time Series Analysis Andrea Beccarini Center for Quantitative Economics Winter 2013/2014 Andrea Beccarini (CQE) Time Series Analysis Winter 2013/2014 1 / 143 Introduction Objectives Time series are ubiquitous

More information

Chapter 9: Univariate Time Series Analysis

Chapter 9: Univariate Time Series Analysis Chapter 9: Univariate Time Series Analysis In the last chapter we discussed models with only lags of explanatory variables. These can be misleading if: 1. The dependent variable Y t depends on lags of

More information

PRICING OF FOREIGN CURRENCY OPTIONS IN THE SERBIAN MARKET

PRICING OF FOREIGN CURRENCY OPTIONS IN THE SERBIAN MARKET ECONOMIC ANNALS, Volume LIV, No. 180, January March 2009 UDC: 3.33 ISSN: 0013-3264 COMMUNICATIONS Irena Janković* DOI:10.2298/EKA0980091J PRICING OF FOREIGN CURRENCY OPTIONS IN THE SERBIAN MARKET ABSTRACT:

More information

Chapter 7 The ARIMA Procedure. Chapter Table of Contents

Chapter 7 The ARIMA Procedure. Chapter Table of Contents Chapter 7 Chapter Table of Contents OVERVIEW...193 GETTING STARTED...194 TheThreeStagesofARIMAModeling...194 IdentificationStage...194 Estimation and Diagnostic Checking Stage...... 200 Forecasting Stage...205

More information

Univariate Time Series Analysis; ARIMA Models

Univariate Time Series Analysis; ARIMA Models Econometrics 2 Spring 25 Univariate Time Series Analysis; ARIMA Models Heino Bohn Nielsen of4 Outline of the Lecture () Introduction to univariate time series analysis. (2) Stationarity. (3) Characterizing

More information

16 : Demand Forecasting

16 : Demand Forecasting 16 : Demand Forecasting 1 Session Outline Demand Forecasting Subjective methods can be used only when past data is not available. When past data is available, it is advisable that firms should use statistical

More information

Working Papers. Cointegration Based Trading Strategy For Soft Commodities Market. Piotr Arendarski Łukasz Postek. No. 2/2012 (68)

Working Papers. Cointegration Based Trading Strategy For Soft Commodities Market. Piotr Arendarski Łukasz Postek. No. 2/2012 (68) Working Papers No. 2/2012 (68) Piotr Arendarski Łukasz Postek Cointegration Based Trading Strategy For Soft Commodities Market Warsaw 2012 Cointegration Based Trading Strategy For Soft Commodities Market

More information

Hedge ratio estimation and hedging effectiveness: the case of the S&P 500 stock index futures contract

Hedge ratio estimation and hedging effectiveness: the case of the S&P 500 stock index futures contract Int. J. Risk Assessment and Management, Vol. 9, Nos. 1/2, 2008 121 Hedge ratio estimation and hedging effectiveness: the case of the S&P 500 stock index futures contract Dimitris Kenourgios Department

More information

Working Paper A joint analysis of the KOSPI 200 option and ODAX option markets dynamics

Working Paper A joint analysis of the KOSPI 200 option and ODAX option markets dynamics econstor www.econstor.eu Der Open-Access-Publikationsserver der ZBW Leibniz-Informationszentrum Wirtschaft The Open Access Publication Server of the ZBW Leibniz Information Centre for Economics Cao, Ji;

More information

Module 5: Multiple Regression Analysis

Module 5: Multiple Regression Analysis Using Statistical Data Using to Make Statistical Decisions: Data Multiple to Make Regression Decisions Analysis Page 1 Module 5: Multiple Regression Analysis Tom Ilvento, University of Delaware, College

More information

Serhat YANIK* & Yusuf AYTURK*

Serhat YANIK* & Yusuf AYTURK* LEAD-LAG RELATIONSHIP BETWEEN ISE 30 SPOT AND FUTURES MARKETS Serhat YANIK* & Yusuf AYTURK* Abstract The lead-lag relationship between spot and futures markets indicates which market leads to the other.

More information

Multiple Linear Regression

Multiple Linear Regression Multiple Linear Regression A regression with two or more explanatory variables is called a multiple regression. Rather than modeling the mean response as a straight line, as in simple regression, it is

More information

The Impact of Foreign Aid on Economic Growth of Ethiopia (Through Transmission Channels)

The Impact of Foreign Aid on Economic Growth of Ethiopia (Through Transmission Channels) International Journal of Business and Economics Research 2015; 4(3): 121-132 Published online May 19, 2015 (http://www.sciencepublishinggroup.com/j/ijber) doi: 10.11648/j.ijber.20150403.15 ISSN: 2328-7543

More information

The Orthogonal Response of Stock Returns to Dividend Yield and Price-to-Earnings Innovations

The Orthogonal Response of Stock Returns to Dividend Yield and Price-to-Earnings Innovations The Orthogonal Response of Stock Returns to Dividend Yield and Price-to-Earnings Innovations Vichet Sum School of Business and Technology, University of Maryland, Eastern Shore Kiah Hall, Suite 2117-A

More information

BACKTESTING VALUE AT RISK MODELS IN THE PRESENCE OF STRUCTURAL BREAK ON THE ROMANIAN AND HUNGARIAN STOCK MARKETS

BACKTESTING VALUE AT RISK MODELS IN THE PRESENCE OF STRUCTURAL BREAK ON THE ROMANIAN AND HUNGARIAN STOCK MARKETS BACKTESTING VALUE AT RISK MODELS IN THE PRESENCE OF STRUCTURAL BREAK ON THE ROMANIAN AND HUNGARIAN STOCK MARKETS Zapodeanu Daniela, Kulcsar Edina, Cociuba Mihail Ioan University of Oradea, Faculty of Economics,

More information

y t by left multiplication with 1 (L) as y t = 1 (L) t =ª(L) t 2.5 Variance decomposition and innovation accounting Consider the VAR(p) model where

y t by left multiplication with 1 (L) as y t = 1 (L) t =ª(L) t 2.5 Variance decomposition and innovation accounting Consider the VAR(p) model where . Variance decomposition and innovation accounting Consider the VAR(p) model where (L)y t = t, (L) =I m L L p L p is the lag polynomial of order p with m m coe±cient matrices i, i =,...p. Provided that

More information

THE PRICE OF GOLD AND STOCK PRICE INDICES FOR

THE PRICE OF GOLD AND STOCK PRICE INDICES FOR THE PRICE OF GOLD AND STOCK PRICE INDICES FOR THE UNITED STATES by Graham Smith November 2001 Abstract This paper provides empirical evidence on the relationship between the price of gold and stock price

More information

Booth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay. Solutions to Midterm

Booth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay. Solutions to Midterm Booth School of Business, University of Chicago Business 41202, Spring Quarter 2015, Mr. Ruey S. Tsay Solutions to Midterm Problem A: (30 pts) Answer briefly the following questions. Each question has

More information

Comovements of the Korean, Chinese, Japanese and US Stock Markets.

Comovements of the Korean, Chinese, Japanese and US Stock Markets. World Review of Business Research Vol. 3. No. 4. November 2013 Issue. Pp. 146 156 Comovements of the Korean, Chinese, Japanese and US Stock Markets. 1. Introduction Sung-Ky Min * The paper examines Comovements

More information

REASSESSMENT OF SUSTAINABILITY OF CURRENT ACCOUNT DEFICIT IN INDIA

REASSESSMENT OF SUSTAINABILITY OF CURRENT ACCOUNT DEFICIT IN INDIA South-Eastern Europe Journal of Economics 1 (2012) 67-79 REASSESSMENT OF SUSTAINABILITY OF CURRENT ACCOUNT DEFICIT IN INDIA AVIRAL KUMAR TIWARI * ICFAI University, Tripura Abstract In this study, we examined

More information

The following postestimation commands for time series are available for regress:

The following postestimation commands for time series are available for regress: Title stata.com regress postestimation time series Postestimation tools for regress with time series Description Syntax for estat archlm Options for estat archlm Syntax for estat bgodfrey Options for estat

More information