Assessment of probabilistic forecasts: Proper scoring rules and moments
The article provides an overview of probabilistic forecasting and discusses a theoretical approach to assessing the quality of density forecasts, based on proper scoring rules and moments. An artificial example of predicting second-order autoregression and an example of predicting RTSI stock index are used to try out this approach.
References listed on IDEAS
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- Gneiting, Tilmann, 2011. "Making and Evaluating Point Forecasts," Journal of the American Statistical Association, American Statistical Association, vol. 106(494), pages 746-762.
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- Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268.
- Gneiting, Tilmann & Raftery, Adrian E., 2007. "Strictly Proper Scoring Rules, Prediction, and Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 359-378, March.
- James Mitchell & Kenneth F. Wallis, 2011. "Evaluating density forecasts: forecast combinations, model mixtures, calibration and sharpness," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(6), pages 1023-1040, 09.
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- Tsyplakov, Alexander, 2011. "Evaluating density forecasts: a comment," MPRA Paper 31184, University Library of Munich, Germany.
- Eduardo Rossi, 2010. "Univariate GARCH models: a survey (in Russian)," Quantile, Quantile, issue 8, pages 1-67, July. Full references (including those not matched with items on IDEAS)
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