The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models

Abstract

This paper proposes a method to implement maximum likelihood estimation of the dynamic panel data type 2 and 3 tobit models. The likelihood function involves a two-dimensional indefinite integral evaluated using two-step Gauss-Hermite quadrature. A Monte Carlo study shows that the quadrature works well in finite sample for a number of evaluation points as small as two. Incorrectly ignoring the individual effects, or the dependence between the initial conditions and the individual effects results in an overestimation of the coefficients of the lagged dependent variables. An application to incremental and radical product innovations by Dutch business firms illustrates the method.