Combining p-values for Multivariate Predictive Ability Testing

Lars Spreng and Giovanni Urga

Journal of Business & Economic Statistics, 2023, vol. 41, issue 3, 765-777

Abstract: In this article, we propose an intersection-union test for multivariate forecast accuracy based on the combination of a sequence of univariate tests. The testing framework evaluates a global null hypothesis of equal predictive ability using any number of univariate forecast accuracy tests under arbitrary dependence structures, without specifying the underlying multivariate distribution. An extensive Monte Carlo simulation exercise shows that our proposed test has very good size and power properties under several relevant scenarios, and performs well in both low- and high-dimensional settings. We illustrate the empirical validity of our testing procedure using a large dataset of 84 daily exchange rates running from January 1, 2011 to April 1, 2021. We show that our proposed test addresses inconclusive results that often arise in practice.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/07350015.2022.2067545 (text/html)
Access to full text is restricted to subscribers.

Related works:
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:taf:jnlbes:v:41:y:2023:i:3:p:765-777

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/UBES20

DOI: 10.1080/07350015.2022.2067545

Access Statistics for this article

Journal of Business & Economic Statistics is currently edited by Eric Sampson, Rong Chen and Shakeeb Khan

More articles in Journal of Business & Economic Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().