Estimation of Censored Quantile Regression for Panel Data With Fixed Effects
Antonio F. Galvao,
Carlos Lamarche and
Luiz Lima ()
Journal of the American Statistical Association, 2013, vol. 108, issue 503, 1075-1089
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.
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