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The Relative Power of Zero-Padding When Testing for Serial Correlation Using Artificial Regressions

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  • Belsley, David A

Abstract

Artificial regression allows a simple and flexible test of serial correlation with many virtues that promote it, in principle, to a position of dominance. But it has a serious small-sample problem: successively truncated lagged residual regressors reduce limited d. of f. twice, simultaneously reducing T and increasing K. It is therefore with interest that one learns one can pad-out the truncated residuals with zeros and the test remains asymptotically valid. Of course, asymptotic virtues are small comfort with limited d. of f., so one wonders about the small-sample effectiveness of this zero-padding procedure. The Monte Carlo study presented here addresses this issue: in a sample of size 20, there appears little, if any, gain to zero-padding and, indeed, in the most common cases, zero-padding results in marginally reduced power. Citation Copyright 1996 by Kluwer Academic Publishers.

Suggested Citation

  • Belsley, David A, 1996. "The Relative Power of Zero-Padding When Testing for Serial Correlation Using Artificial Regressions," Computational Economics, Springer;Society for Computational Economics, vol. 9(3), pages 181-198, August.
    • Handle: RePEc:kap:compec:v:9:y:1996:i:3:p:181-98
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