Bias-Corrected Quantile Regression Estimation of Censored Regression Models

CentER Discussion Paper Series No. 2014-060

45 Pages Posted: 7 Oct 2014

See all articles by Pavel Cizek

Pavel Cizek

Tilburg University - Department of Econometrics & Operations Research

Serhan Sadikoglu

Tilburg University - Department of Econometrics & Operations Research

Date Written: October 6, 2014

Abstract

Motivated by weak small-sample performance of the censored regression quantile estimator proposed by Powell (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.

Keywords: asymptotic normality, censored regression, indirect inference, quantile regression

JEL Classification: C21, C24

Suggested Citation

Cizek, Pavel and Sadikoglu, Serhan, Bias-Corrected Quantile Regression Estimation of Censored Regression Models (October 6, 2014). CentER Discussion Paper Series No. 2014-060, Available at SSRN: https://ssrn.com/abstract=2505995 or http://dx.doi.org/10.2139/ssrn.2505995

Pavel Cizek (Contact Author)

Tilburg University - Department of Econometrics & Operations Research ( email )

Tilburg, 5000 LE
Netherlands

Serhan Sadikoglu

Tilburg University - Department of Econometrics & Operations Research ( email )

Tilburg, 5000 LE
Netherlands

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