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Monte Carlo Evidence on Cointegration and Causation

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  • Zapata, Hector O
  • Rambaldi, Alicia N

Abstract

The small sample performance of Granger causality tests under different model dimensions, degree of cointegration, direction of causality, and system stability are presented. Two tests based on maximum likelihood estimation of error-correction models (LR and WALD) are compared to a Wald test based on multivariate least squares estimation of a modified VAR (MWALD). In large samples, all test statistics perform well in terms of size and power. For smaller samples, the LR and WALD tests perform better than the MWALD test. Overall, the LR test outperforms the other two in terms of size and power in small samples. Copyright 1997 by Blackwell Publishing Ltd

Suggested Citation

  • Zapata, Hector O & Rambaldi, Alicia N, 1997. "Monte Carlo Evidence on Cointegration and Causation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 59(2), pages 285-298, May.
  • Handle: RePEc:bla:obuest:v:59:y:1997:i:2:p:285-98
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    1. Lutkepohl, Helmut & Reimers, Hans-Eggert, 1992. "Granger-causality in cointegrated VAR processes The case of the term structure," Economics Letters, Elsevier, vol. 40(3), pages 263-268, November.
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    8. Mosconi, Rocco & Giannini, Carlo, 1992. "Non-causality in Cointegrated Systems: Representation Estimation and Testing," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 54(3), pages 399-417, August.
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