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

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  • A. Ronald Gallant
  • Han Hong
  • Ahmed Khwaja

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.

Suggested Citation

  • A. Ronald Gallant & Han Hong & Ahmed Khwaja, 2012. "Bayesian Estimation of a Dynamic Game with Endogenous, Partially Observed, Serially Correlated State," Working Papers 12-01, Duke University, Department of Economics.
    • Handle: RePEc:duk:dukeec:12-01
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    More about this item

    Keywords

    Dynamic Games; Partially Observed State; Endogenous State; Serially Correlated State; Particle Filter;
    All these keywords.

    JEL classification:

    • E00 - Macroeconomics and Monetary Economics - - General - - - General
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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