Working Paper #2007-007
The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models
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.
- Keywords
-
- panel data
- maximum likelihood estimator
- dynamic models
- sample selection
- JEL codes
-
- C33
- C34
- O31
- UNU-MERIT
- Working
- Papers
- ISSN
- 1871-9872
Cite this as
APA 7th ed.Raymond, W., Mohnen, P., Palm, F., & Schim van der Loeff, S. (2007). The Behavior of the Maximum Likelihood Estimator of Dynamic Panel Data Sample Selection Models (UNU-MERIT Working Paper No. 2007-007). UNU-MERIT. https://unu-merit.nl/publications/working-papers/abstract/?id=2819