Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189319 
Year of Publication: 
2004
Series/Report no.: 
Queen's Economics Department Working Paper No. 1035
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
Abstract: 
We 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.
Subjects: 
bootstrap test
bootstrap discrepancy
Pitman drift
drifting DGP
Monte Carlo
test power
power
asymptotic test
JEL: 
C12
C15
Document Type: 
Working Paper

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