Bayesian Estimation of a Dynamic Game with Endogenous, Partially Observed, Serially Correlated State
AbstractWe 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.
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Bibliographic InfoPaper provided by Duke University, Department of Economics in its series Working Papers with number 12-01.
Date of creation: 2012
Date of revision:
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Postal: Department of Economics Duke University 213 Social Sciences Building Box 90097 Durham, NC 27708-0097
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Dynamic Games; Partially Observed State; Endogenous State; Serially Correlated State; Particle Filter;
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-05-15 (All new papers)
- NEP-ECM-2012-05-15 (Econometrics)
- NEP-GTH-2012-05-15 (Game Theory)
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