Quantile Autoregression for Censored Data

Quantile autoregression (QAR) is particularly attractive for censored data. However, unlike the standard regression models, the autoregressive models must take account of censoring on both response and regressors. In this article, we show that the existing censored quantile regression methods produce consistent estimators for QAR models when using only the fully observed regressors. A new algorithm is proposed to provide a censored QAR estimator by adopting imputation methods. The algorithm redistributes probability mass of censored points appropriately and iterates towards self-consistent solutions. Monte Carlo simulations and empirical applications are conducted to demonstrate merits of the proposed method.