An Exponential Class of Dynamic Binary Choice Panel Data Models with Fixed Effects

Abstract

This paper develops a model for dynamic binary choice panel data that allows for unobserved heterogeneity to be arbitrarily correlated with covariates. The model is of the exponential type. We derive moment conditions that enable us to eliminate the unobserved heterogeneity term and at the same time to identify the parameters of the model. We then propose GMM estimators that are consistent and asymptotically normally distributed at the root-N rate. We also study the conditional likelihood approach, which can only identify the effect of state dependence in our case. Monte Carlo experiments demonstrate the finite sample performance of our GMM estimators.

Keywords: dynamic discrete choice, fixed effects, panel data, initial values, GMM, CMLE

JEL Classification: C23, C25