The Power of Bootstrap and Asymptotic Tests
AbstractWe introduce the concept of the bootstrap discrepancy, which measures the difference in rejection probabilities between a bootstrap test based on a given test statistic and that of a (usually infeasible) test based on the true distribution of the statistic. We show that the bootstrap discrepancy is of the same order of magnitude under the null hypothesis and under non-null processes described by a Pitman drift. However, complications arise in the measurement of power. If the test statistic is not an exact pivot, critical values depend on which data-generating process (DGP) is used to determine the distribution under the null hypothesis. We propose as the proper choice the DGP which minimizes the bootstrap discrepancy. We also show that, under an asymptotic independence condition, the power of both bootstrap and asymptotic tests can be estimated cheaply by simulation. The theory of the paper and the proposed simulation method are illustrated by Monte Carlo experiments using the logit model.
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Bibliographic InfoPaper provided by Queen's University, Department of Economics in its series Working Papers with number 1035.
Length: 22 pages
Date of creation: Jul 2004
Date of revision:
Publication status: Forthcoming in Journal of Econometrics
bootstrap test; bootstrap discrepancy; Pitman drift; drifting DGP; Monte Carlo; test power; power; asymptotic test;
Other versions of this item:
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2006-03-11 (All new papers)
- NEP-ECM-2006-03-11 (Econometrics)
- NEP-ETS-2006-03-11 (Econometric Time Series)
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