Asymptotic Inference for Score Decompositions

Add to your list(s) Download to your calendar using vCal

• Timo Dimitriadis (Goethe-Universität Frankfurt)
• Thursday 05 June 2025, 09:30-09:50
• Seminar Room 1, Newton Institute.

If you have a question about this talk, please contact nobody.

RCLW02 - Calibrating prediction uncertainty : statistics and machine learning perspectives

Economic and statistical forecasts are regularly assessed by strictly consistent scoring functions such as the mean squared error for mean, or the tick loss for quantile forecasts. While scoring functions allow for statistically meaningful rankings of overall forecast quality, they are silent about the specific deficiencies of the forecasts. We develop asymptotic inference for recently proposed score decompositions into miscalibration, discrimination and uncertainty terms, which allows for hypothesis tests and the construction of confidence intervals for the miscalibration and discrimination terms. These methods deliver more detailed insights into forecast performances in applications to mean forecasts for inflation rates and volatility forecasts in risk management.   Co-author: Marius Puke, University of Hohenheim

This talk is part of the Isaac Newton Institute Seminar Series series.

This talk is included in these lists:

• All CMS events
• Featured lists
• INI info aggregator
• Isaac Newton Institute Seminar Series
• School of Physical Sciences
• Seminar Room 1, Newton Institute
• bld31

Note that ex-directory lists are not shown.