Goodness-of-fit testing in regression: A finite sample comparison of bootstrap methodology and Khmaladze transformation

Hira L. Koul and Lyudmila Sakhanenko

Statistics & Probability Letters, 2005, vol. 74, issue 3, 290-302

Abstract: It is well known that the tests based on the residual empirical process for fitting an error distribution in regression models are not asymptotically distribution free. One either uses a Monte-Carlo method or a bootstrap method to implement them. Another option is to base tests on the Khmaladze transformation of these processes because it renders them asymptotically distribution free. This note compares Monte-Carlo, naive bootstrap, and the smooth bootstrap methods of implementing the Kolmogorov-Smirnov test with the Khmaladze transformed test. We find that the transformed test outperforms the naive and smooth bootstrap methods in preserving the level. The note also includes a power comparison of these tests.

Keywords: Naive; and; smooth; bootstrap; Martingale; transform; Level; Power (search for similar items in EconPapers)
Date: 2005
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Citations: View citations in EconPapers (6)

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