Simple solutions to the initial conditions problem in dynamic, nonlinear panel data models with unobserved heterogeneity

TLDR

The study proposes a simple, widely applicable method to address the initial conditions problem in dynamic, nonlinear models with unobserved effects. The method derives the conditional distribution given the initial value and exogenous history, enabling simple estimators for average partial effects in special cases. The approach yields straightforward estimation strategies for probit, Tobit, and Poisson models and provides simple estimators for average partial effects in key special cases. © 2005 John Wiley & Sons, Ltd.

Abstract

Abstract I study a simple, widely applicable approach to handling the initial conditions problem in dynamic, nonlinear unobserved effects models. Rather than attempting to obtain the joint distribution of all outcomes of the endogenous variables, I propose finding the distribution conditional on the initial value (and the observed history of strictly exogenous explanatory variables). The approach is flexible, and results in simple estimation strategies for at least three leading dynamic, nonlinear models: probit, Tobit and Poisson regression. I treat the general problem of estimating average partial effects, and show that simple estimators exist for important special cases. Copyright © 2005 John Wiley & Sons, Ltd.