Bootstrap Critical Values for Tests Based on the Smoothed Maximum Score Estimator
The smoothed maximum score estimator of the coefficient vector of a binary response model is consistent and asymptotically normal under weak distributional assumptions. However, the differences between the true and nominal levels of tests based on smoothed maximum score estimates can be very large in finite samples when first- order asymptotics are used to obtain critical values. This paper shows that the bootstrap provides finite-sample critical values that are more accurate than those obtained from first-order asymptotic theory. In a set of Monte Carlo experiments carried out to check numerical performance, the bootstrap essentially eliminates large finite- sample distortions of level that occur when asymptotic critical values are used.
|Date of creation:||07 Mar 1996|
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