Data Imputation Methods and Technologies

We introduce a class of linear quantile estimators for panel data. Our framework contains dynamic autoregressive models, models with general predetermined regressors, and models with multiple individual effects as special cases. We follow a correlated random-effects approach, and rely additional layers of quantile regressions as a flexible tool to model conditional distributions. Conditions are given under which the model is nonparametrically identified in static or Markovian dynamic models. We develop a sequential method-of-moment a estimation, and compute the estimator using an iterative algorithm that exploits the computational simplicity of ordinary quantile regression in each iteration step. Finally, a Monte-Carlo exercise and an application to measure the effect of smo pregnancy on children's birthweights complete the paper.