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 David K. Levine in its series Levine's Working Paper Archive with number 584.
Date of creation: 09 Dec 2010
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Other versions of this item:
- Ehud Kalai, 1995. "Calibrated Forecasting and Merging," Discussion Papers 1144R, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Ehud Kalai, 1995. "Calibrated Forecasting and Merging," Discussion Papers 1144, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- NEP-ALL-2010-12-18 (All new papers)
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