Estimation of Censored Quantile Regression for Panel Data With Fixed Effects

• 成果类型: Article
• 署名作者: Galvao, Antonio F.; Lamarche, Carlos; Lima, Luiz Renato
• 署名单位: University of Iowa; University of Kentucky; University of Tennessee System; University of Tennessee Knoxville; Universidade Federal da Paraiba
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2013.818002
• 发表日期: 2013
• 页码: 1075-1089
• 关键词: BIAS REDUCTION; models

摘要

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 conduct Monte 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.