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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- Russell Davidson & James G. MacKinnon, 1994.
"Graphical Methods for Investigating the Size and Power of Hypothesis Tests,"
903, Queen's University, Department of Economics.
- 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.
- Davidson, R. & Mackinnon, J.G., 1997.
"Bootstrap Testing in Nonlinear Models,"
97a39, Universite Aix-Marseille III.
- Davidson, R. & Mackinnon, J.G., 1996.
"The Size Distorsion of Bootstrap Tests,"
96a15, Universite Aix-Marseille III.
- Russell Davidson & James G. MacKinnon, 1982.
"Convenient Specification Tests for Logit and Probit Models,"
514, Queen's University, Department of Economics.
- 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.
- Horowitz, Joel L., 1994. "Bootstrap-based critical values for the information matrix test," Journal of Econometrics, Elsevier, vol. 61(2), pages 395-411, April.
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