Identification and Efficient Semiparametric Estimation of a Dynamic Discrete Game

Patrick Bajari, Victor Chernozhukov, Han Hong and Denis Nekipelov ()

No 21125, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: In this paper, we study the identification and estimation of a dynamic discrete game allowing for discrete or continuous state variables. We first provide a general nonparametric identification result under the imposition of an exclusion restriction on agent payoffs. Next we analyze large sample statistical properties of nonparametric and semiparametric estimators for the econometric dynamic game model. We also show how to achieve semiparametric efficiency of dynamic discrete choice models using a sieve based conditional moment framework. Numerical simulations are used to demonstrate the finite sample properties of the dynamic game estimators. An empirical application to the dynamic demand of the potato chip market shows that this technique can provide a useful tool to distinguish long term demand from short term demand by heterogeneous consumers.

JEL-codes: C01 C14 C57 C7 C73 L0 (search for similar items in EconPapers)
Date: 2015-04
New Economics Papers: this item is included in nep-dcm, nep-ecm, nep-gth and nep-ore
Note: IO TWP
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Citations: View citations in EconPapers (16)

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