Estimation of dynamic discrete models from time aggregated data

Abstract

An important component in dynamic discrete choice models and dynamic discrete games is the transition density of state variables from the current period to the next period. Most empirical dynamic discrete choice models identify the theoretical time interval in the behavioral model with that observed in the data set. However, many empirical data sets are time aggregated. In this paper, we show that when the time interval in the behavioral theory model differs from that in the observed data, difficulties with nonparametric identification and specification arise. In addition, we study the properties of parametric maximum likelihood estimators and flexible semiparametric estimators of the transition density in dynamic discrete models with time aggregated data sets.