Markov chain Monte Carlo for incomplete information discrete games

Sanjog Misra

Quantitative Marketing and Economics (QME), 2013, vol. 11, issue 1, No 5, 117-153

Abstract: Abstract This paper outlines a Bayesian approach to estimating discrete games of incomplete information. The MCMC routine proposed features two changes to the traditional Metropolis–Hastings algorithm to facilitate the estimation of games. First, we propose a new approach to sample equilibrium probabilities using a probabilistic equilibrium selection rule that allows for the evaluation of the parameter posterior. Second, we propose a differential evolution based MCMC sampler which is capable of handling the unwieldy posterior that only has support on the equilibrium manifold. We also present two applications to demonstrate the feasibility of our proposed methodology.

Keywords: Discrete games; MCMC; Nested fixed point; Differential evolution (search for similar items in EconPapers)
JEL-codes: C11 C7 L2 M3 (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:kap:qmktec:v:11:y:2013:i:1:d:10.1007_s11129-012-9128-5

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DOI: 10.1007/s11129-012-9128-5

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