The bootstrap and the Edgeworth correction for semiparametric averaged derivatives

Yoshihiko Nishiyama and Peter M. Robinson

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

Abstract: In a number of semiparametric models, smoothing seems necessary in order to obtain estimates of the parametric component which are asymptotically normal and converge at parametric rate. However, smoothing can inflate the error in the normal approximation, so that refined approximations are of interest, especially in sample sizes that are not enormous. We show that a bootstrap distribution achieves a valid Edgeworth correction in case of density-weighted averaged derivative estimates of semiparametric index models. Approaches to bias-reduction are discussed. We also develop a higher order expansion, to show that the bootstrap achieves a further reduction in size distortion in case of two-sided testing. The finite sample performance of the methods is investigated by means of Monte Carlo simulations from a Tobit model.

Keywords: Bootstrap; Edgeworth correction; semiparametric averaged derivatives (search for similar items in EconPapers)
JEL-codes: C14 C24 (search for similar items in EconPapers)
Pages: 47 pages
Date: 2005-01
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Citations: View citations in EconPapers (16)

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Related works:
Journal Article: The Bootstrap and the Edgeworth Correction for Semiparametric Averaged Derivatives (2005)
Working Paper: The Bootstrap and the Edgeworth Correction for Semiparametric Averaged Derivatives (2005)
Working Paper: The bootstrap and the Edgeworth correction for semiparametric averaged derivatives (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:ehl:lserod:2297

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