Score-based calibration testing for multivariate forecast distributions

Malte Kn\"uppel, Fabian Kr\"uger and Marc-Oliver Pohle
Authors registered in the RePEc Author Service: Malte Knüppel

Papers from arXiv.org

Abstract: Calibration tests based on the probability integral transform (PIT) are routinely used to assess the quality of univariate distributional forecasts. However, PIT-based calibration tests for multivariate distributional forecasts face various challenges. We propose two new types of tests based on proper scoring rules, which overcome these challenges. They arise from a general framework for calibration testing in the multivariate case, introduced in this work. The new tests have good size and power properties in simulations and solve various problems of existing tests. We apply the tests to forecast distributions for macroeconomic and financial time series data.

Date: 2022-11, Revised 2023-12
New Economics Papers: this item is included in nep-ecm, nep-for and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://arxiv.org/pdf/2211.16362 Latest version (application/pdf)

Related works:
Working Paper: Score-based calibration testing for multivariate forecast distributions (2022)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2211.16362

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().