Scaling and Measurement Error Sensitivity of Scoring Rules for Distribution Forecasts

51 Pages Posted: 8 Nov 2019 Last revised: 8 Aug 2023

See all articles by Onno Kleen

Onno Kleen

Erasmus University Rotterdam (EUR) - Erasmus School of Economics (ESE); Tinbergen Institute

Date Written: July 4, 2023

Abstract

This paper examines the impact of data rescaling and measurement error on scoring rules for distribution forecast. First, I show that all commonly used scoring rules for distribution forecasts are robust to rescaling the data. Second, 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, a simulation study aligned with financial market volatility, 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 (July 4, 2023). 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

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

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