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Evaluating Probabilistic Forecasts with scoringRules
Alexander I. Jordan (University of Bern), Fabian Krüger (Heidelberg University), Sebastian Lerch (Karlsruhe Institute of Technology, Heidelberg Institute for Theoretical Studies)
Abstract: Probabilistic forecasts in the form of probability distributions over future events have become popular in several fields including meteorology, hydrology, economics, and demography. In typical applications, many alternative statistical models and data sources can be used to produce probabilistic forecasts. Hence, evaluating and selecting among competing methods is an important task. The scoringRules package for R provides functionality for comparative evaluation of probabilistic models based on proper scoring rules, covering a wide range of situations in applied work. This paper discusses implementation and usage details, presents case studies from meteorology and economics, and points to the relevant background literature.