Estimating Dynamic Games of Oligopolistic Competition: An Experimental Investigation

Abstract

Dynamic oligopoly estimators have become a workhorse for industry studies in empirical industrial organization. We evaluate parameter recovery and counterfactual predictions for such environments using laboratory data. In our experimental setting we characterize a symmetric Markov-perfect equilibrium and also a non-Markov equilibrium that allows for much higher payoffs. We estimate structural parameters under the standard assumption that the data are generated by a Markov-perfect equilibrium and then use the estimates to predict counterfactual behavior. The concern is that if the Markov assumption is violated in the data, we would find biased estimates large errors in counterfactual predictions. The experimental method allows us to compare estimated parameters to the true induced parameters, and counterfactual predictions to true counterfactuals implemented as treatments. Our main finding is that restricting attention to Markov-perfect equilibria at the estimation stage is, in fact, not very restrictive.