Identification and Estimation of Continuous Time Dynamic Discrete Choice Games

Jason R. Blevins.
The Ohio State University, Department of Economics
Working Paper.

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Abstract. 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 provide more general conditions for existence of equilibrium which allow move arrival rates to differ across firms and model states. Next, we return to the question of nonparametric identification of the intensity matrix and the structural primitives of the model when only discrete-time data are available. We show that the conditions for identification are easily verifiable in representative example models. Finally, we examine both the computational properties of the model and the finite sample performance of a two-step semiparametric estimator for the model through a series of Monte Carlo experiments in a simple single agent renewal model and a dynamic oligopoly quality ladder model.

Keywords: Continuous time, Markov decision processes, dynamic discrete choice, dynamic games, identification.

JEL Classification: C13, C35, C62, C73.

BibTeX Record:

  author       = {Jason R. Blevins},
  title        = {Identification and Estimation of Continuous Time
                  Dynamic Discrete Choice Games},
  institution  = {Ohio State University},
  year         = 2016,
  type         = {Unpublished Manuscript}