PPML Estimation of Dynamic Discrete Choice Models with Aggregate Shocks

Abstract

This paper introduces a computationally efficient method for estimating structural parameters of dynamic discrete choice models with large choice sets. The method is based on Poisson pseudo maximum likelihood (PPML) regression, which is widely used in the international trade and migration literature to estimate the gravity equation. Unlike most of the existing methods in the literature, it does not require strong parametric assumptions on agents' expectations, thus it can accommodate macroeconomic and policy shocks. The regression requires count data as opposed to choice probabilities; therefore it can handle sparse decision transition matrices caused by small sample sizes. As an example application, the paper estimates sectoral worker mobility in the United States.