Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments
AbstractThe paper provides an overview of probabilistic forecasting and discusses a theoretical framework for evaluation of probabilistic forecasts which is based on proper scoring rules and moments. An artificial example of predicting second-order autoregression and an example of predicting the RTSI stock index are used as illustrations.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 45186.
Date of creation: 18 Mar 2013
Date of revision:
probabilistic forecast; forecast calibration; probability integral transform; scoring rule; moment condition;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ALL-2013-03-23 (All new papers)
- NEP-ECM-2013-03-23 (Econometrics)
- NEP-FOR-2013-03-23 (Forecasting)
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