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Identification and Estimation of Games with Incomplete Information Using Excluded Regressors

Arthur Lewbel and Xun Tang
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Xun Tang: University of Pennsylvania

No 808, Boston College Working Papers in Economics from Boston College Department of Economics

Abstract: The existing literature on binary games with incomplete information assumes that either payoff functions or the distribution of private information are finitely parameterized to obtain point identification. In contrast, we show that, given excluded regressors, payoff functions and the distribution of private information can both be nonparametrically point identified. An excluded regressor for player i is a sufficiently varying state variable that does not affect other players' utility and is additively separable from other components in i's payoff. We show how excluded regressors satisfying these conditions arise in contexts such as entry games between firms, as variation in observed components of fixed costs. Our identification proofs are constructive, so consistent nonparametric estimators can be readily based on them. For a semiparametric model with linear payoffs, we propose root-N consistent and asymptotically normal estimators for parameters in players' payoffs. Finally, we extend our approach to accommodate the existence of multiple Bayesian Nash equilibria in the data-generating process without assuming equilibrium selection rules.

Keywords: Games with Incomplete Information; Excluded Regressors; Nonparametric Identification; Semiparametric Estimation; Multiple Equilibria. (search for similar items in EconPapers)
JEL-codes: C14 C51 D43 (search for similar items in EconPapers)
Date: 2012-08-21, Revised 2013-03-05
New Economics Papers: this item is included in nep-cta, nep-ecm and nep-gth
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Journal Article: Identification and estimation of games with incomplete information using excluded regressors (2015) Downloads
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