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THE SIZE DISTORTION OF BOOTSTRAP TESTS
Open accessAbstract
We provide a theoretical framework in which to study the accuracy of bootstrap P values, which may be based on a parametric or nonparametric bootstrap. In the parametric case, the accuracy of a bootstrap test will depend on the shape of what we call the critical value function. We show that, in many circumstances, the error in rejection probability of a bootstrap test will be one whole order of magnitude smaller than that of the corresponding asymptotic test. We also propose a simulation method for estimating this error that requires the calculation of only two test statistics per replication.
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Cited by
Bootstrap-Based Improvements for Inference With Clustered Errors
Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008) · Review of Economics and Statistics · doi:10.1162/rest.90.3.414 · Cited by 4,417
Fast and wild: Bootstrap inference in Stata using boottest
Roodman, D., Nielsen, M. Ø., MacKinnon, J. G., & Webb, M. D. (2019) · The Stata Journal: Promoting communications on statistics and Stata · doi:10.1177/1536867x19830877 · Cited by 1,007
Self-citationBootstrap Methods for Time Series
Härdle, W., Horowitz, J., & Kreiss, J. (2003) · International Statistical Review · doi:10.1111/j.1751-5823.2003.tb00485.x · Cited by 294
The Wild Bootstrap with a “Small” Number of “Large” Clusters
Canay, I. A., Santos, A., & Shaikh, A. M. (2021) · The Review of Economics and Statistics · doi:10.1162/rest_a_00887 · Cited by 104
Metadata
- Authors
- Davidson, R., & MacKinnon, J. G.
- Venue
- Econometric Theory
- Publisher
- Cambridge University Press (CUP)
- Published
- 1999-06
- Volume
- 15
- Issue
- 3
- Pages
- 361-376
- ISSN
- 0266-4666 (ISSN-L)
- 1469-4360
- OpenAlex
- W2158933431
- Cited by
- 206
- References
- 0
- FWCI
- 14.68
- Type
- journal article
- Language
- en
- Open access
- green
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- Davidson, R., & MacKinnon, J. G. (1999). THE SIZE DISTORTION OF BOOTSTRAP TESTS. Econometric Theory. https://doi.org/10.1017/s0266466699153040
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
