Semiparametric estimation of a censored regression model with endogeneity

Songnian Chen and Qian Wang

Journal of Econometrics, 2020, vol. 215, issue 1, 239-256

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.

Keywords: Censored regression; Endogeneity; Semiparametric estimation (search for similar items in EconPapers)
JEL-codes: C14 C21 C24 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407619301848
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:215:y:2020:i:1:p:239-256

DOI: 10.1016/j.jeconom.2019.08.006

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

More articles in Journal of Econometrics from Elsevier
Bibliographic data for series maintained by Catherine Liu ().