Testing for equal predictive accuracy with strong dependence

Laura Coroneo and Fabrizio Iacone

International Journal of Forecasting, 2025, vol. 41, issue 3, 1073-1092

Abstract: We analyse the properties of the Diebold and Mariano (1995) test in the presence of autocorrelation in the loss differential. We show that the power of the Diebold and Mariano (1995) test decreases as the dependence increases, making it more difficult to obtain statistically significant evidence of superior predictive ability against less accurate benchmarks. We also find that, after a certain threshold, the test has no power, and the correct null hypothesis is spuriously rejected. These results caution us to seriously consider the loss differential’s dependence properties before applying the Diebold and Mariano (1995) test.

Keywords: Strong autocorrelation; Forecast evaluation; Equal predictive accuracy; Diebold and Mariano test; Time series (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207024001067
Full text for ScienceDirect subscribers only

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:eee:intfor:v:41:y:2025:i:3:p:1073-1092

DOI: 10.1016/j.ijforecast.2024.11.003

Access Statistics for this article

International Journal of Forecasting is currently edited by R. J. Hyndman

More articles in International Journal of Forecasting from Elsevier
Bibliographic data for series maintained by Catherine Liu ().