Semiparametric estimation of a censored regression model with endogeneity

Songnian Chen, Qian Wang

Research output: Journal PublicationArticlepeer-review

2 Citations (Scopus)

Abstract

Censoring and endogeneity are common in empirical applications. However, the existing semiparametric estimation methods for the censored regression model with endogeneity under an independence restriction are associated with some drawbacks. In this paper we propose a new semiparametric estimator that overcomes these drawbacks. We derive conditional quantile moment conditions for all the conditional quantiles and propose a moment-based estimator. In particular, we construct two types of moment conditions and develop a computationally attractive estimator. We show that our estimator is consistent and asymptotic normal. A Monte Carlo study indicates that our estimator performs well in finite samples and compares favorably with existing methods.

Original languageEnglish
Pages (from-to)239-256
Number of pages18
JournalJournal of Econometrics
Volume215
Issue number1
DOIs
Publication statusPublished - Mar 2020
Externally publishedYes

Keywords

  • Censored regression
  • Endogeneity
  • Semiparametric estimation

ASJC Scopus subject areas

  • Economics and Econometrics

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