The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models
39 Pages Posted: 11 Jun 2007
Date Written: May 2007
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.
JEL Classification: C34, C51, O33
Suggested Citation: Suggested Citation