Estimation of a Panel Data Sample Selection Model

Abstract

We consider the problem of estimation in a panel data sample selection model, where both the selection and the regression equation of interest contain unobservable individual-specific effects. We propose a two-step estimation procedure, which differences out both the sample selection effect and the unobservable individual effect from the equation of interest. In the first step, the unknown coefficients of the selection equation are consistently estimated. The estimates are then used to estimate the regression equation of interest. The estimator proposed in this paper is consistent and asymptotically normal, with a rate of convergence that can be made arbitrarily close to n -1/2 , depending on the strength of certain smoothness assumptions. The finite sample properties of the estimator are investigated in a small Monte Carlo simulation.