Identification and Estimation of Dynamic Games with Unknown Information Structure

TL;DR

This paper introduces a new empirical framework for analyzing dynamic games with unknown information structures, using Markov correlated equilibrium to match predictions with richer signal observations and providing robust estimation methods.

Contribution

It develops the concept of Markov correlated equilibrium for dynamic games with unknown information, enabling robust estimation and inference under informational uncertainty.

Findings

The framework aligns equilibrium predictions with richer signal observations.

Provides tractable methods for estimation and counterfactual analysis.

Applied to a US entry game between Starbucks and Dunkin'.

Abstract

We develop an empirical framework for analyzing dynamic games when the underlying information structure is unknown to the analyst. We introduce \textit{Markov correlated equilibrium}, a dynamic analog of Bayes correlated equilibrium, and show that its predictions coincide with the Markov perfect equilibrium predictions attainable when players observe richer signals than the analyst assumes. We provide tractable methods for informationally robust estimation, inference, and counterfactual analysis. We illustrate the framework with a dynamic entry game between Starbucks and Dunkin' in the US and study the role of informational assumptions.