The Size And Power Of Bootstrap Tests

James MacKinnon and Russell Davidson

No 932, Working Paper from Economics Department, Queen's University

Abstract: Bootstrap tests are tests for which the significance level is calculated by some sort of bootstrap procedure, which may be parametric or nonparametric. We show that, in many circumstances, the size distortion of a bootstrap P value for a test will be one whole order of magnitude smaller than that of the corresponding asymptotic P value. We also show that, at least in the parametric case, the magnitude of the distortion will depend on the shape of what we call the P value function. As regards the power of bootstrap tests, we show that the size-corrected power of a bootstrap test differs from that of the corresponding asymptotic test only by an amount of the same order of magnitude as the size distortion, and of arbitrary sign. Monte Carlo results are presented for two cases of interest: tests for serial correlation and nonnested hypothesis tests. These results confirm and illustrate the utility of our theoretical results, and they also suggest that bootstrap tests will often work extremely well in practice.

Keywords: tests for serial correlation; bootstrapping; hypothesis testing; Non-nested hypothesis tests; P values (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 41 pages
Date: 1996-02
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (16)

Downloads: (external link)
https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_932.pdf First version 1996 (application/pdf)

Related works:
Working Paper: The Size and Power of Bootstrap Tests (1997)
Working Paper: The Size and Power of Bootstrap Tests (1996)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:932

Access Statistics for this paper

More papers in Working Paper from Economics Department, Queen's University Contact information at EDIRC.
Bibliographic data for series maintained by Mark Babcock ().