THE JOHANSEN-JUSELIUS MULTIVARIATE COINTEGRATION TECHNIQUE: APPLICATION AND INTERPRETATION

Authors

  • EMEKA NKORO UNIVERSITY OF PORT HARCOURT, PORT HARCOURT, RIVERS STATE, NIGERIA
  • AHAM KELVIN UKO UNIVERSITY OF PORT HARCOURT, PORT HARCOURT, RIVERS STATE, NIGERIA

DOI:

https://doi.org/10.14738/assrj.34.1961

Keywords:

Cointegration, Unit Roots, the Johansen-Juselius multivariate technique, Error Correction mechanism

Abstract

Cointegration is the simplest way of detrending series whose mean, variance as well as autocorrelation functions changes over time due to the presence of unit roots. However, the issue has shifted to the application of the appropriate cointegration technique as various cointegration techniques abound. Hence, this study reviews the issues surrounding the application and interpretation cointegration techniques within the context of Johansen-Juselius multivariate cointegration framework. The study shows that the adoption of the Johansen-Juselius multivariate cointegration technique rests on the pretests for unit roots. The study reveals that Johansen-Juselius multivariate cointegration technique is preferable when dealing with more than two variables that are integrated of the same and as well different order, I(d) .However, the Johansen-Juselius multivariate cointegration technique is robust when dealing with variables of the same order of integration. The number of cointegrating vectors is detected through the two likelihood ratio test statistics (trace and maximum test). Although the major difficulty lies in the identification of the cointegrating vectors where there are multiple cointegrating vectors. In this approach, cointegration is said to be established when there is at least one cointegrating vector. Based on forecast and policy implications, this paper explores the conditions that necessitate the application of the Johansen and Juselius cointegration technique. This is to avoid its wrongful application, which may in turn lead to model misspecification, inconsistent and unrealistic estimates. However, this paper cannot claim to have treated the underlying issues in their greatest details, but have endeavoured to provide sufficient insight into the issues surrounding Johansen and Juselius cointegration technique to practitioners to enable them apply and interpret and also, follow discussions of the issues in some more advanced texts.

References

Asteriou, Dimitrios and Hall, Stephen G. (2007). Applied Econometrics: A Modern Approach, PALGRAVE MACMILLAN, New York.

Banerjee, A., J. Dolado, J. W. Galbraith, and H. David (1993): Co-integration, Error Correction

and the Econometric Analysis of non-Stationary Data. Oxford University Press, USA.

Bo Sj¨o (2008): Testing for Unit Roots and Cointegration, Memo.

Brandt, P. T. and J.T. Williams (2006): Multiple Time Series Models. Series: Quantitative Applications in the Social Sciences. 148, Sage Publications Ltd, London.

Dickey, D. and W. Fuller (1979): Distribution of the Estimators for Autoregressive Time Series with a Unit Root, Journal of the American Statistical Association 74, 427-431.

-----------(1981): Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root, Econometrica 49, 1057-1072.

Enders, W. (2004): Applied Econometric Time Series, 2nd Ed, John Wiley & Sons Inc, New York.

Engle, R. F. (1982): Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of UK Inflation, Econometrica, 50, 987-1007.

Engle, R. and G. Granger (1987): Cointegration and Error Correction: Representation, Estimation and Testing, Econometrica 55, 251-276.

Geweke,J., Meese, R. and Dent, W. (1983): Comparing Alternative Tests of Causality in Temporal System, Journal of Econometrics, 77, Pp 161-194.

Godfery, L.G (1988): Misspecification Test in Econometrics: The Lagrange Multiplier Principle and Other Approaches, Cambridge University press, Cambridge.

Granger, C. W. J. (1969): Investigating Causal Relations by Econometric Models and Cross Spectral Methods, Econometrica, 37, 428-438.

Granger, C. (1981): Some Properties of Time Series Data and Their Use in Econometric Model Specification. Journal of Econometrics 28, 121-130.

Granger, C.W.J. (1983): CoIntegrated Variables and Error-Correcting Models, UCSD Discussion, Paper 83-13.

------------(1986): Developments in the Study of Cointegrated Economic Variables, Oxford Bulletin of Economics and Statistics 48, 213-228.

----------(1988): Some Recent Developments in a Concept of Causality, Journal of Econometrics 39, 199-211.

Granger, C. and P. Newbold,(1974): Spurious Regressions in Econometrics, Journal of Econometrics 26, 1045-1066.

Granger, C.W.J. and Weiss, A.A (1983): Time Series Analysis of Error-Correcting Models, in: Studies in Econometrics, Time Series and Multivariate Statistics. Academic Press: New York, 255-78.

Gujarati, D.N. (2004): Basic Econometrics, Fourth Edtion, Tata McGraw-Hill Publishing Company Limited, New Delhi.

Johansen, S. (1988): Statistical Analysis of Cointegration Vectors, Journal of Economic Dynamics and Control 12, 231-254.

Johansen, S. (1995): Likelihood Based Inference in Cointegrated Vector Autoregressive Models, Oxford University Press: Oxford.

Johansen, S and K. Juselius(1992): Testing Structural Hypotheses in Multivariate Cointegration Analysis of the PPP and UIP for Uk, Journal of Econometrica, 53, Pp 211-44

Johansen, S. and K. Juselius (1988) : Hypothesis Testing for Cointegration Vectors with an Application to the Demand for Money in Denmark and Finland, Working Paper No. 88-05, University of Copenhagen.

Johansen, S. and K. Juselius (1990): Maximum Likelihood Estimation and Inference on Cointegration-With Applications to the Demand for Money, Oxford Bulletin of Economics and Statistics, 52(2), 169-210.

Lütkepohl,H. and , P. Saikkonen (200):Testing for the Cointegrating Rank of a VAR Process with a Time Trend, Journal of Econometrics 95, 177-198.

Maddalas, G.S(1992): Introduction to Econometrics, 2nd Ed, Englewood Cliffs, Prentice Hall.

Pesaran, M.H. and B. Pesaran (1996a). Microfit 4.0, Cambridge London, Windows Version, CAMFIT DATA LIMITED

Phillips, P, and P. Perron (1988): “Testing for a unit root in time series regression.” Bimetrika, 75: 335-346.

Quang, T.V. (2007) :Testing Cointegration for Czech Stock Market, Center for Research in Economic Dynamics and Econometrics.

Ruxanda, G and A. Botezatu (2008): Spurious Regression and Cointegration. Numerical Example: Romania’s M2 Money Demand, Romanian Journal of Economic Forecast, 3.

Seddighi, H.R, K.A. Lawler and A.V. Katos (2000): Econometrics: A Practical Approach, Routledge, London EC4P4EE.

Uko, A.K and E. Nkoro (2012): Inflation Forecast with ARIMA, Vector Autoregressive and Error Correction Models in Nigeria, EJEFAS, Issue 50, July.

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Published

2016-04-29

How to Cite

NKORO, E., & UKO, A. K. (2016). THE JOHANSEN-JUSELIUS MULTIVARIATE COINTEGRATION TECHNIQUE: APPLICATION AND INTERPRETATION. Advances in Social Sciences Research Journal, 3(4). https://doi.org/10.14738/assrj.34.1961

Issue

Vol. 3 No. 4 (2016): Advances in Social Sciences Research Journal

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Articles

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