A loss discounting framework for model averaging and selection in time series models

Dawid Bernaciak and Jim E. Griffin

International Journal of Forecasting, 2024, vol. 40, issue 4, 1721-1733

Abstract: We introduce a loss discounting framework for model and forecast combination, which generalises and combines Bayesian model synthesis and generalized Bayes methodologies. We use a loss function to score the performance of different models and introduce a multilevel discounting scheme that allows for a flexible specification of the dynamics of the model weights. This novel and simple model combination approach can be easily applied to large-scale model averaging/selection, handle unusual features such as sudden regime changes and be tailored to different forecasting problems. We compare our method to established and state-of-the-art methods for several macroeconomic forecasting examples. The proposed method offers an attractive, computationally efficient alternative to the benchmark methodologies and often outperforms more complex techniques.

Keywords: Bayesian model synthesis; Density forecasting; Forecast combination; Forecast averaging; Multilevel discounting (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0169207024000268
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:40:y:2024:i:4:p:1721-1733

DOI: 10.1016/j.ijforecast.2024.03.001

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 ().