The bootstrap and the Edgeworth correction for semiparametric averaged derivatives

Yoshihiko Nishiyama and Peter Robinson

No CWP12/04, CeMMAP working papers from Centre for Microdata Methods and Practice, Institute for Fiscal Studies

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 froma Tobit model.

JEL-codes: C23 (search for similar items in EconPapers)
Pages: 44 pp.
Date: 2004-10-01
New Economics Papers: this item is included in nep-ecm
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Citations: View citations in EconPapers (6)

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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 (2005)
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