Calibration and Bayesian learning
By: Nurlan Turdaliev.
Contributors: Federal Reserve Bank of Minneapolis. Research Dept.
Series: Working paper / Federal Reserve Bank of Minneapolis, Research Dept. ;; 596; Working paper (Federal Reserve Bank of Minneapolis : Online) ;; 596.
Publisher: Federal Reserve Bank of Minneapolis
Published: 1999
Place of publication: [Minneapolis, Minn.]
Language: eng
Format: Electronic resource
Subjects: Bayesian statistical decision theory.; Game theory.; Economics, Mathematical.
Description: "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"--Federal Reserve Bank of Minneapolis web site.
Notes: Also available in print.
Includes bibliographical references.
Title from abstract page as viewed on 1/5/2005.
"September 1999."
System requirements: Adobe Acrobat Reader.
Mode of access: World Wide Web.