Calibration and Bayesian learning
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.
|Date of creation:||1999|
|Date of revision:|
|Contact details of provider:|| Postal: |
Phone: (612) 204-5000
Web page: http://minneapolisfed.org/
More information through EDIRC
|Order Information:|| Web: http://www.minneapolisfed.org/pubs/ Email: |
When requesting a correction, please mention this item's handle: RePEc:fip:fedmwp:596. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Janelle Ruswick)
If references are entirely missing, you can add them using this form.