Bregman model averaging for forecast combination

Yi-Ting Chen, Chu-An Liu and Jiun-Hua Su

Journal of Econometrics, 2025, vol. 251, issue C

Abstract: We propose a unified model averaging (MA) approach for a broad class of forecasting targets. This approach is established by minimizing an asymptotic risk based on the expected Bregman divergence of a combined forecast, relative to the optimal forecast of the forecasting target, under local(-to-zero) asymptotics. It can be flexibly applied to develop effective MA methods across various forecasting contexts, including but not limited to univariate and multivariate mean forecasting, volatility forecasting, probabilistic forecasting, and density forecasting. As illustrative examples, we present a series of simulation experiments and empirical cases that demonstrate strong numerical performance of our approach in forecasting.

Keywords: Bregman divergence; Forecast combination; Loss function; Model averaging (search for similar items in EconPapers)
JEL-codes: C18 C32 C53 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304407625001307
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:econom:v:251:y:2025:i:c:s0304407625001307

DOI: 10.1016/j.jeconom.2025.106076

Access Statistics for this article

Journal of Econometrics is currently edited by T. Amemiya, A. R. Gallant, J. F. Geweke, C. Hsiao and P. M. Robinson

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