Bayesian Estimation of a Dynamic Game with Endogenous, Partially Observed, Serially Correlated State

A. Gallant, Han Hong and Ahmed Khwaja

No 12-01, Working Papers from Duke University, Department of Economics

Abstract: We consider dynamic games that can have state variables that are partially observed, serially correlated, endogenous, and heterogeneous. We propose a Bayesian method that uses a particle filter to compute an unbiased estimate of the likelihood within a Metropolis chain. Unbiasedness guarantees that the stationary density of the chain is the exact posterior, not an approximation. The number of particles required is easily determined. The regularity conditions are weak. Results are verified by simulation from two dynamic oligopolistic games with endogenous state. One is an entry game with feedback to costs based on past entry and the other a model of an industry with a large number of heterogeneous firms that compete on product quality.

Keywords: Dynamic Games; Partially Observed State; Endogenous State; Serially Correlated State; Particle Filter (search for similar items in EconPapers)
JEL-codes: C51 C52 E00 G12 (search for similar items in EconPapers)
Pages: 38
Date: 2012
New Economics Papers: this item is included in nep-ecm and nep-gth
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Persistent link: https://EconPapers.repec.org/RePEc:duk:dukeec:12-01

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