Improving the Reliability of Bootstrap Tests
We first propose procedures for estimating the rejection probabilities for bootstrap tests in Monte Carlo experiments without actually computing a bootstrap test for each replication. These procedures are only about twice as expensive as estimating rejection probabilities for asymptotic tersts. We then propose procedures for computing modified bootstrap P values that will often be more accurate than ordinary ones. These procedures are closely related to the double bootstrap, but they are far less computationally demanding.
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- Davidson, Russell & MacKinnon, James G., 1999.
"The Size Distortion Of Bootstrap Tests,"
Cambridge University Press, vol. 15(03), pages 361-376, June.
- Davidson, Russell & MacKinnon, James G, 1999.
"Bootstrap Testing in Nonlinear Models,"
International Economic Review,
Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 487-508, May.
- Davidson, Russell & MacKinnon, James G., 1984.
"Convenient specification tests for logit and probit models,"
Journal of Econometrics,
Elsevier, vol. 25(3), pages 241-262, July.
- Russell Davidson & James G. MacKinnon, 1982. "Convenient Specification Tests for Logit and Probit Models," Working Papers 514, Queen's University, Department of Economics.
- Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
- Davidson, Russell & MacKinnon, James G, 1998.
"Graphical Methods for Investigating the Size and Power of Hypothesis Tests,"
The Manchester School of Economic & Social Studies,
University of Manchester, vol. 66(1), pages 1-26, January.
- Russell Davidson & James G. MacKinnon, 1994. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," Working Papers 903, Queen's University, Department of Economics.
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