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Calibration and Bayesian learning

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  • Nurlan Turdaliev

Abstract

In a repeated game of incomplete information, myopic players form beliefs on next-period play and choose strategies to maximize next-period payoffs. Beliefs are treated as forecast of future plays. Forecast accuracy is assessed using calibration tests, which measure asymptotic accuracy of beliefs against some realizations. Beliefs are calibrated if they pass all calibration tests. For a positive Lebesgue measure of payoff vectors, beliefs are not calibrated. But, if payoff vector and calibration test are drawn from a suitable product measure, beliefs pass the calibration test almost surely.

Suggested Citation

  • Nurlan Turdaliev, 1999. "Calibration and Bayesian learning," Working Papers 596, Federal Reserve Bank of Minneapolis.
  • Handle: RePEc:fip:fedmwp:596
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    References listed on IDEAS

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    Keywords

    Forecasting;

    Statistics

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