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The bootstrap and the Edgeworth expansion for semiparametric averaged derivatives

Nishiyama, Y. and Robinson, Peter (2005) The bootstrap and the Edgeworth expansion for semiparametric averaged derivatives. Econometrica, 73 (3). pp. 903-948. ISSN 0012-9682

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Identification Number: 10.1111/j.1468-0262.2005.00598.x


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 the 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 the case of two-sided testing. The finite-sample performance of the methods is investigated by means of Monte Carlo simulations from a Tobit model.

Item Type: Article
Official URL:
Additional Information: © 2005 The Econometric Society
Divisions: Economics
Subjects: H Social Sciences > HB Economic Theory
JEL classification: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods
Date Deposited: 27 Jun 2007
Last Modified: 14 Feb 2024 00:09

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