Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments

The 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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Year of publication:	2013-03-18
Authors:	Tsyplakov, Alexander
Institutions:	Volkswirtschaftliche Fakultät, Ludwig-Maximilians-Universität München
Subject:	probabilistic forecast \| forecast calibration \| probability integral transform \| scoring rule \| moment condition

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Extent:	application/pdf
Series:	MPRA Paper.
Type of publication:	Book / Working Paper
Classification:	C52 - Model Evaluation and Testing ; C53 - Forecasting and Other Model Applications
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10011113537