Calibrated Forecasting and Merging
AbstractConsider a general finite-state stochastic process governed by an unknown objective probability distribution. Observing the system, a forecaster assigns subjective probabilities to future states. The resulting subjective forecast merges to the objective distribution if, with time, the forecasted probabilities converge to the correct (but unknown) probabilities. The forecast is calibrated if observed long-run empirical distributions coincide with the forecasted probabilities. This paper links the unobserved reliability of forecasts to their observed empirical performance by demonstrating full equilvalence between notions of merging and of calibration. It also indicates some implications of this equilvalence for the literatures of forecasting and learning.
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Bibliographic InfoPaper provided by Northwestern University, Center for Mathematical Studies in Economics and Management Science in its series Discussion Papers with number 1144R.
Date of creation: Dec 1995
Date of revision:
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Postal: Center for Mathematical Studies in Economics and Management Science, Northwestern University, 580 Jacobs Center, 2001 Sheridan Road, Evanston, IL 60208-2014
Web page: http://www.kellogg.northwestern.edu/research/math/
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Other versions of this item:
- Ehud Kalai, 1995. "Calibrated Forecasting and Merging," Discussion Papers 1144, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Ehud Kalai & Ehud Lehrer & Rann Smorodinsky, 2010. "Calibrated Forecasting and Merging," Levine's Working Paper Archive 584, David K. Levine.
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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Game Theory and Information
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