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Bayesian estimation of discrete games of complete information

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  • Sridhar Narayanan

Abstract

Estimation of discrete games of complete information, which have been applied to a variety of contexts such as market entry, technology adoption and peer effects, is challenging due to the presence of multiple equilibria. In this paper, we take a Bayesian MCMC approach to this problem, specifying a prior over multiple equilibrium selection mechanisms reflecting the analysts uncertainty over them. We develop a sampler, using the reversible jump algorithm to generate draws from the posterior distribution of parameters across these equilibrium selection rules. The algorithm is flexible in that it can be used both in situations where the equilibrium selection rule is identified and when it is not, and accommodates heterogeneity in equilibrium selection. We explore the methodology using both simulated data and two empirical applications, one in the context of joint consumption, using a dataset of casino visit decisions by married couples, and the second in the context of market entry by competing chains in the retail stationery market. We demonstrate the importance of accounting for multiple equilibrium selection rules in these applications and show that taking an empirical approach to the issue, such as the one we have demonstrated, can be useful. Copyright Springer Science+Business Media, LLC 2013

Suggested Citation

  • Sridhar Narayanan, 2013. "Bayesian estimation of discrete games of complete information," Quantitative Marketing and Economics (QME), Springer, vol. 11(1), pages 39-81, March.
    • Handle: RePEc:kap:qmktec:v:11:y:2013:i:1:p:39-81
    DOI: 10.1007/s11129-012-9127-6
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    Cited by:

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    2. Ron Borkovsky & Paul Ellickson & Brett Gordon & Victor Aguirregabiria & Pedro Gardete & Paul Grieco & Todd Gureckis & Teck-Hua Ho & Laurent Mathevet & Andrew Sweeting, 2015. "Multiplicity of equilibria and information structures in empirical games: challenges and prospects," Marketing Letters, Springer, vol. 26(2), pages 115-125, June.
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    4. Hee Mok Park & Puneet Manchanda, 2015. "When Harry Bet with Sally: An Empirical Analysis of Multiple Peer Effects in Casino Gambling Behavior," Marketing Science, INFORMS, vol. 34(2), pages 179-194, March.
    5. Arun Gopalakrishnan & Eric T. Bradlow & Peter S. Fader, 2017. "A Cross-Cohort Changepoint Model for Customer-Base Analysis," Marketing Science, INFORMS, vol. 36(2), pages 195-213, March.

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    More about this item

    Keywords

    Discrete games; Multiple equilibria; Bayesian estimation; Markov Chain Monte Carlo methods; Reversible jump algorithm; C11; C31; C35; M31;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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