Estimation of censored quantile regression for panel data with fixed effects

Antonio F. Galvao, Carlos Lamarche, Luiz Renato Lima

Research output: Contribution to journalArticlepeer-review

52 Scopus citations


This article investigates estimation of censored quantile regression (QR) models with fixed effects. Standard available methods are not appropriate for estimation of a censored QR model with a large number of parameters or with covariates correlated with unobserved individual heterogeneity. Motivated by these limitations, the article proposes estimators that are obtained by applying fixed effects QR to subsets of observations selected either parametrically or nonparametrically. We derive the limiting distribution of the new estimators under joint limits, and conductMonte Carlo simulations to assess their small sample performance. An empirical application of the method to study the impact of the 1964 Civil Rights Act on the black-white earnings gap is considered. Supplementary materials for this article are available online.

Original languageEnglish
Pages (from-to)1075-1089
Number of pages15
JournalJournal of the American Statistical Association
Issue number503
StatePublished - 2013

Bibliographical note

Funding Information:
Antonio F. Galvao is Associate Professor, Department of Economics, University of Iowa, W210 Pappajohn Business Building, 21 E. Market Street, Iowa City, IA 52242 (E-mail: Carlos Lamarche is Associate Professor, Department of Economics, University of Kentucky, 335A Gatton College of Business and Economics, Lexington, KY 40506-0034 (E-mail: Luiz Renato Lima is Associate Professor, Department of Economics, University of Tennessee, 527A Stokely Management Center, Knoxville, TN 37996; and Federal University of Paraiba-Brazil (E-mail: We are grateful to Ivan Canay, Joel Horowitz, Roger Koenker, Stephen Portnoy, Michael Price, Liang Wang, and seminar participants at the University of Illinois at Urbana-Champaign, Northwestern University, University of Kentucky, University of Tennessee, NY Camp Econometrics VIII, and the 21st meeting of the Midwest Econometric Group for useful comments and discussion regarding this article. We also thank the editor, the associate editor, and three anonymous referees for their careful reading and comments to improve the article. We thank Marcelo Moreira for providing the data for the empirical section. Luiz Lima thanks financial support from CNPq-Brazil. The R software for the method introduced in this article is available upon request.


  • Civil right
  • Earnings gap
  • Fixed censoring
  • Individual heterogeneity
  • Longitudinal data

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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