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Some Finite Sample Results On Testing For Granger Noncausality

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Abstract

We compare testing strategies for Granger noncausality in vector autoregressions (VARs)that may or may not have unit roots and cointegration. Sequential testing methods are examined; these test for cointegration and use either a differenced VAR or a vector error correction model(VECM), in which to undertake the main noncausality test. Basically, these strategies attempt to verify the validity of appropriate standard limit theory. We contrast such methods with an augmented lag approach that ensures the limiting Chi Square null distribution irrespective of the data’s nonstationarity characteristics. Our simulations involve bivariate and trivariate VARs in which we allow for the lag order to be selected by general to specific testing as well as by model selection criteria. We find that the current practice of pretesting for cointegration can result in severe over-rejections of the noncausal null while overfitting results in better control of the Type I error probability with often little loss in power.

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Bibliographic Info

Paper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 0305.

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Length: 35 pages
Date of creation: 30 May 2003
Date of revision:
Handle: RePEc:vic:vicewp:0305

Note: ISSN 1485-6441
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Keywords: cointegration; error correction model; lag length selection; sequential testing; testing for Granger noncausality; Monte Carlo simulation;

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  24. Hoffman, Dennis L & Rasche, Robert H, 1996. "Assessing Forecast Performance in a Cointegrated System," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(5), pages 495-517, Sept.-Oct.
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Cited by:
  1. repec:ltr:wpaper:2007.02 is not listed on IDEAS
  2. László Kónya & Jai Pal Singh, 2007. "Causality between Indian Exports, Imports, and Agricultural, Manufacturing GDP," Working Papers 2007.02, School of Economics, La Trobe University.

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