Estimation and Inference in Games of Incomplete Information with Unobserved Heterogeneity and Large State Space

Speaker: Dr Xuetao Shi

Affiliation: The University of Sydney

Location: Room 101 - Chamberlain Building (#35)

Abstract:

This paper studies sequential estimation and inference in static games of incomplete information with payoff-relevant unobserved heterogeneity and multiple equilibria based on the identification framework of Aguirregabiria and Mira (2019). With payoff-relevant unobserved heterogeneity, sequential estimation and inference face two main challenges: the matching-types problem and a large number of matchings. We tackle the matching-types problem by constructing a new minimum-distance characterization of the correct matching and the payoff function with both correct and incorrect “moments”. To handle large numbers of matchings, we propose a novel and consistent multistep moment selection procedure. The new procedure is extremely fast to execute and achieves linear time complexity in the number of “moments” asymptotically in the absence of multiple equilibria. Based on it, we construct a consistent estimator of the payoff function and an asymptotically uniformly valid and easy-to-implement test for linear hypotheses on the payoff function. Via extensive simulations, we demonstrate that the multistep moment selection procedure is extremely fast to implement, and that both the estimator and test have good finite sample performance.

About the presenter:

If you would like to meet with Dr Shi, please contact Dr Fu Ouyang