 Calculating effective degrees of freedom for forecast combinations and ensemble modelsJames Younker Economics Letters, 2025, vol. 247, issue C Abstract: Forecast combinations, also known as ensemble models, routinely require practitioners to select a model from a massive number of potential candidates. Ten explanatory variables can be grouped into 21078 forecast combinations, and the number of possibilities increases further to 21078+21078 if we allow for forecast combinations of forecast combinations. This paper derives a calculation for the effective degrees of freedom of a forecast combination under a set of general conditions for linear models. It also supports this calculation with simulations. The result allows users to perform several other computations, including the F-test and various information criteria. These computations are particularly useful when there are too many candidate models to evaluate out of sample. Furthermore, computing effective degrees of freedom shows that the complexity cost of a forecast combination is driven by the parameters in the weighting scheme and the weighted average of parameters in the auxiliary models as opposed to the number of auxiliary models. This identification of complexity cost contributions can help practitioners make informed choices about forecast combination design. JEL-codes: C C01 C02 C1 C13 C5 C50 C51 C52 C53 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecolet:v:247:y:2025:i:c:s0165176524006219 DOI: 10.1016/j.econlet.2024.112137 Access Statistics for this article Economics Letters is currently edited by Economics Letters Editorial Office More articles in Economics Letters from Elsevier |
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