The Exact Powers of Some Autocorrelation Tests When Relevant Regressions are Omitted

J. P. Small, D. E. A. Giles and K. J. White

No 263722, Department of Economics Discussion Papers from University of Canterbury - New Zealand

Abstract: We consider the power functions of five popular tests for AR(1) errors in a linear regression model from which relevant regressors have inadvertently been omitted. These functions are derived by numerically evaluating the finite-sample distributions of the test statistics. With this form of model mis-specification, it is found that the performances of the tests are not independent of the scale of the errors' distribution. The omission of seasonal effects or a linear trend component can have serious implications, especially if testing against positive autocorrelation, and some of the well known advantages of the "Alternative Durbin Watson test" (King (1981)) are found to still apply when the model is underspecified.

Keywords: Research; Methods/Statistical; Methods (search for similar items in EconPapers)
Pages: 24
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Persistent link: https://EconPapers.repec.org/RePEc:ags:canzdp:263722

DOI: 10.22004/ag.econ.263722

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