A New Estimator for Panel Data Sample Selection Models

Abstract

In this paper we are concerned with the estimation of a panel data sample selection model where both the selection and the regression equation contain individual effects allowed to be correlated with the observable variables. In this direction, some estimation techniques have been recently developed. We propose a new method for correcting for sample selection bias. Our estimation procedure is an extension of the familiar two-step sample selection technique to the case where one correlated selection rule in two different time periods generates the sample. Some non-parametric components are allowed. The finite sample properties of the estimator are investigated by Monte Carlo simulation experiments.