Bootstrap critical values for tests based on the smoothed maximum score estimator

Joel L. Horowitz*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

26 Scopus citations

Abstract

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 gives conditions under which the differences between the true and nominal levels can be reduced by using the bootstrap to obtain critical values. A set of Monte Carlo experiments illustrates the numerical performance of the bootstrap.

Original languageEnglish (US)
Pages (from-to)141-167
Number of pages27
JournalJournal of Econometrics
Volume111
Issue number2
DOIs
StatePublished - Dec 2002
EventFinite Sample and Asymptotic Methods in Econometrics - Amsterdam, Netherlands
Duration: Dec 11 1997Dec 11 1997

Keywords

  • Asymptotic refinement
  • Binary response
  • Edgeworth expansion
  • Hypothesis test

ASJC Scopus subject areas

  • Economics and Econometrics

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