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Testing Structural Hypotheses on Cointegration Vectors: A Monte Carlo Study

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Abstract

In this paper, two tests for structural hypotheses on cointegration vectors are evaluated in a Monte Carlo study. The tests are the likelihood ratio test proposed by Johansen (1991) and the test for stationarity proposed by Kwiatkowski et al (1992). The analysis of the likelihood ratio test is extended with the inclusion of a Bartlett correction factor. Under circumstances common in empirical applications, all tests suffer from large size distortions and have low power to detect a false cointegration vector, but the Johansen (1991) test fares slightly better than the Kwiatkowski et al (1992) test. Applying a Bartlett correction factor in small samples improves to a large extent the likelihood ratio test.

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  • Eriksson , Åsa, 2004. "Testing Structural Hypotheses on Cointegration Vectors: A Monte Carlo Study," Working Papers 2004:29, Lund University, Department of Economics.
  • Handle: RePEc:hhs:lunewp:2004_029
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    1. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "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, Elsevier, vol. 54(1-3), pages 159-178.
    2. Gredenhoff, Mikael & Jacobson, Tor, 2001. "Bootstrap Testing Linear Restrictions on Cointegrating Vectors," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(1), pages 63-72, January.
    3. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    4. Zhou, Su, 2000. "Testing Structural Hypotheses on Cointegration Relations with Small Samples," Economic Inquiry, Western Economic Association International, vol. 38(4), pages 629-640, October.
    5. Johansen, Søren, 2000. "A Bartlett Correction Factor For Tests On The Cointegrating Relations," Econometric Theory, Cambridge University Press, vol. 16(5), pages 740-778, October.
    6. Omtzigt Pieter & Fachin Stefano, 2002. "Bootstrapping and Bartlett corrections in the cointegrated VAR model," Economics and Quantitative Methods qf0212, Department of Economics, University of Insubria.
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    9. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
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    11. Haug, Alfred A., 2002. "Testing Linear Restrictions On Cointegrating Vectors: Sizes And Powers Of Wald And Likelihood Ratio Tests In Finite Samples," Econometric Theory, Cambridge University Press, vol. 18(2), pages 505-524, April.
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    14. Johansen, Søren & Juselius, Katarina, 1992. "Testing structural hypotheses in a multivariate cointegration analysis of the PPP and the UIP for UK," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 211-244.
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    16. Robert A. Amano & Simon van Norden, 1995. "Unit Root Tests and the Burden of Proof," Econometrics 9502005, University Library of Munich, Germany.
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    Cited by:

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    2. Richard G. Anderson & Hailong Qian & Robert H. Rasche, 2006. "Analysis of panel vector error correction models using maximum likelihood, the bootstrap, and canonical-correlation estimators," Working Papers 2006-050, Federal Reserve Bank of St. Louis.

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    More about this item

    Keywords

    Cointegration; Structural hypothesis; Monte Carlo simulation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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