The bootstrap and the edgeworth correction for semiparametric averaged derivatives

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: cemmap working paper ; No. CWP12/04

Classification and Topics

• Classification: Wirtschaft ; Single Equation Models; Single Variables: Panel Data Models; Spatio-temporal Models
• Subject: Bootstrap , Edgeworth correction , Semiparametric averaged derivatives ; Nichtparametrisches Verfahren ; Theorie ; Bootstrap-Verfahren ; Fehlerkorrekturmodell

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Nishiyama, Yoshihiko ; Robinson, Peter M.
• Event: Veröffentlichung
• (who): Centre for Microdata Methods and Practice (cemmap)
• (where): London
• (when): 2004

Further information

• DOI: doi:10.1920/wp.cem.2004.1204
• Handle: http://hdl.handle.net/10419/79262
• Last update: 10.03.2025, 11:42 AM CET