Bias-corrected quantile regression estimation of censored regression models

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Abstract

In this paper, an extension of the indirect inference methodology to semiparametric estimation is explored in the context of censored regression. Motivated by weak small-sample performance of the censored regression quantile estimator proposed by Powell (J Econom 32:143–155, 1986a), two- and three-step estimation methods were introduced for estimation of the censored regression model under conditional quantile restriction. While those stepwise estimators have been proven to be consistent and asymptotically normal, their finite sample performance greatly depends on the specification of an initial estimator that selects the subsample to be used in subsequent steps. In this paper, an alternative semiparametric estimator is introduced that does not involve a selection procedure in the first step. The proposed estimator is based on the indirect inference principle and is shown to be consistent and asymptotically normal under appropriate regularity conditions. Its performance is demonstrated and compared to existing methods by means of Monte Carlo simulations.
Original languageEnglish
Pages (from-to)215–247
Number of pages33
JournalStatistical Papers
Volume59
Early online date29 Mar 2016
DOIs
Publication statusPublished - 2018

Keywords

  • asymptotic normality
  • censored regression
  • indirect inference
  • quantile regression

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