Identi¯cation and Estimation of Dynamic Games¤

This paper studies the identi¯cation problem in in¯nite horizon Markovian games and pro-poses a generally applicable estimation method. Every period ¯rms simultaneously select an action from a ¯nite set. We characterize the set of Markov equilibria. Period pro¯ts are a linear function of equilibrium choice probabilities. The question of identi¯cation of these values is then reduced to the existence of a solution to this linear equation system. We characterize the identi¯cation conditions. We propose a simple estimation procedure which follows the steps in the identi¯cation argument. The estimator is consistent, asymptotic normally distributed, and e±cient. We have collected quarterly time series data on pubs, restaurants, co®eehouses, bakeries and carpenters for two Austrian towns between 1982 and 2002. A dynamic entry game is estimated in which ¯rms simultaneously decide whether to enter, remain active, or exit the industry. The period pro¯t estimates are used to simulate the equilibrium behavior under a policy experiment in which a unit tax is imposed on ¯rms deciding to enter the industry