Identification and Estimation of Continuous Time Dynamic Games

We consider the theoretical and econometric properties of a recently proposed class of continuous time dynamic games with discrete choices. This class of models has computationally-desirable properties and includes widely-used entry, exit, and investment games that underlie many applications in empirical industrial organization. First, we establish a linear representation of the value functions in the model which assists in both identification and estimation. Next, we examine conditions for nonparametric identification of the intensity matrix and the structural primitives of the model when only discrete-time data are available. Finally, we propose a two-step pseudo maximum likelihood estimator based on a nonparametric estimator for the intensity matrix in the first step and an inverse conditional choice probability mapping used for pseudo maximum likelihood estimation of the structural parameters in the second step.