Scaling and Measurement Error Sensitivity of Scoring Rules for Distribution Forecasts

46 Pages Posted: 8 Nov 2019 Last revised: 30 Mar 2022

See all articles by Onno Kleen

Onno Kleen

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE)

Date Written: March 29, 2022

Abstract

I examine the sensitivity of scoring rules for distribution forecasts in two dimensions: sensitivity to linear rescaling of the data and the influence of measurement error on the forecast evaluation outcome. First, I show that all commonly used scoring rules for distribution forecasts are robust to rescaling the data. Second, it is revealed that the forecast ranking based on the continuous ranked probability score is less sensitive to gross measurement error than the ranking based on the log score. The theoretical results are complemented by a simulation study aligned with frequently revised quarterly US GDP growth data and an empirical application forecasting realized variances of S&P 100 constituents.

Keywords: Forecast evaluation, measurement error, distribution forecasts, proper scoring rules

JEL Classification: C50, C52, C53

Suggested Citation

Kleen, Onno, Scaling and Measurement Error Sensitivity of Scoring Rules for Distribution Forecasts (March 29, 2022). Available at SSRN: https://ssrn.com/abstract=3476461 or http://dx.doi.org/10.2139/ssrn.3476461

Onno Kleen (Contact Author)

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE) ( email )

P.O. Box 1738
3000 DR Rotterdam, NL 3062 PA
Netherlands

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