A0564
Title: Sharp estimation of static entry games with covariates
Authors: Max Antoine Lesellier - University of Montreal (Canada) [presenting]
Abstract: Entry games are a widely used framework for analyzing industry dynamics, using easily accessible data on market entry. New tools are introduced to improve the estimation of static entry games, particularly in scenarios where the equilibrium selection mechanism remains unrestricted. In such settings, the econometrician typically uses inequalities implied by the model to characterize the set of admissible parameters; however, entry games often generate so many inequalities that using them all becomes impractical. To manage this complexity, a recursive algorithm that simultaneously selects a relevant subset of inequalities is developed, and theoretical upper bounds are calculated on the probability of each outcome without relying on simulation-based methods. Additionally, the testing procedure introduced by a prior study is extended, which is consistent and produces an asymptotically pivotal test statistic, to allow for the pre-estimation of the parameters that are point-identified. The approach is particularly useful in handling covariates, including continuous ones. Through comprehensive Monte Carlo simulations, the effectiveness of the estimation procedure is demonstrated. Finally, the method is applied empirically.
