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A Small-Sample Correction for Testing for Joint Serial Correlation with Artificial Regressions

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  • David A. Belsley

Abstract

Prior research (Belsley, 1997) has established that the common tests for single orders of serial correlation (e.g., Durbin–Watson, artificial regression) are badly distorted and result in grossly misleading tests in small samples. A corrected t-statistic has been derived that removes these difficulties, but it cannot be applied to joint tests. This research provides the needed generalizations. First it shows, to no surprise, that the same distortions plague the F-statistic typically used for testing joint orders of serial correlation with artificial regressions. And second it derives a corrected F-statistic that provides acceptable tests for arbitrarily stipulated joint orders of serial correlation. The test procedure is detailed and exemplar code provided.

Suggested Citation

  • David A. Belsley, 2000. "A Small-Sample Correction for Testing for Joint Serial Correlation with Artificial Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 16(1/2), pages 5-45, October.
    • Handle: RePEc:kap:compec:v:16:y:2000:i:1/2:p:5-45
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    Cited by:

    1. Godfrey, L.G. & Tremayne, A.R., 2005. "The wild bootstrap and heteroskedasticity-robust tests for serial correlation in dynamic regression models," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 377-395, April.

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