Nonparametric Identification and Estimation of a Censored Location-Scale Regression Model

Abstract

In this article we consider identification and estimation of a censored nonparametric location scale-model. We first show that in the case where the location function is strictly less than the (fixed) censoring point for all values in the support of the explanatory variables, the location function is not identified anywhere. In contrast, when the location function is greater or equal to the censoring point with positive probability, the location function is identified on the entire support, including the region where the location function is below the censoring point. In the latter case we propose a simple estimation procedure based on combining conditional quantile estimators for various higher quantiles. The new estimator is shown to converge at the optimal nonparametric rate with a limiting normal distribution. A small-scale simulation study indicates that the proposed estimation procedure performs well in finite samples. We also present an empirical illustration on unemployment insurance duration using administrative-level data from New Jersey.