Identification and Estimation of Dynamic Games with Unknown Information Structure

Fuente: arXiv
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Autori principali: Hara, Konan, Ito, Yuki, Koh, Paul
Natura: Preprint
Pubblicazione: 2022
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author Hara, Konan
Ito, Yuki
Koh, Paul
author_facet Hara, Konan
Ito, Yuki
Koh, Paul
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2205_03706
institution arXiv
publishDate 2022
record_format arxiv
spellingShingle Identification and Estimation of Dynamic Games with Unknown Information Structure
Hara, Konan
Ito, Yuki
Koh, Paul
Econometrics
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.
title Identification and Estimation of Dynamic Games with Unknown Information Structure
topic Econometrics
url https://arxiv.org/abs/2205.03706