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

David Belsley

Computing in Economics and Finance 1996 from Society for Computational Economics

Abstract: Monte Carlo experiments establish that the usual ``t-statistic'' used fortesting for first-order serial correlation with artificial regressions is far from being distributed as a Student's t in small samples. Rather, it is badly biased in both mean and variance and results in grossly misleading tests of hypotheses when treated as a Student's t. Simply computed corrections for the mean and variance are derived, however, which are shown to lead to a transformed statistic producing acceptable tests. The test procedure is detailed and exemplar code provided.

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Related works:
Journal Article: A Small-Sample Correction for Testing for gth-Order Serial Correlation with Artificial Regressions (1997)
Working Paper: A Small-Sample Correction for Testing for gth-Order Serial Correlation with Artificial Regressions (1996)
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