Bayesian Analysis of Entry Games: A Simulated Likelihood Approach Without Simulation Errors

Abstract

This paper provides a practical guide to Bayesian estimation of simultaneous entry games of complete information with heterogeneous firms. Bayesian inference requires computation of the likelihood, which is carried out by simulating unobservables. To avoid errors from finite simulations, we apply Andrieu and Roberts's [2009. The pseudo-marginal approach for efficient Monte Carlo computations, Annals of Statistics, 37(2), pp. 697-725] pseudo-marginal approach. We rely also on adaptive Markov chain Monte Carlo algorithms that improve computational performance