Evaluation of Probabilistic Forecasts: Proper Scoring Rules and Moments
MPRA Paper from University Library of Munich, Germany
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.
Keywords: probabilistic forecast; forecast calibration; probability integral transform; scoring rule; moment condition (search for similar items in EconPapers)
JEL-codes: C52 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed
Downloads: (external link)
https://mpra.ub.uni-muenchen.de/45186/1/MPRA_paper_45186.pdf original version (application/pdf)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:45186
Access Statistics for this paper
More papers in MPRA Paper from University Library of Munich, Germany Ludwigstraße 33, D-80539 Munich, Germany. Contact information at EDIRC.
Series data maintained by Joachim Winter ().