On Model Aggregation and Forecast Combination

Nikolay Gospodinov and Esfandiar Massoumi ()

No 2025-12, FRB Atlanta Working Paper from Federal Reserve Bank of Atlanta

Abstract: Policy makers express their views and decisions via the lens of a particular model or theory. But since any model is a highly stylized representation of the unknowable object of interest, all these models are inherently misspecified, and the resulting ambiguity injects uncertainty in the decision-making process. We argue that entropy-based aggregation is a convenient device to confront this uncertainty and summarize relevant information from a set of candidate models and forecasts. The proposed aggregation tends to robustify the decision-making process to various sources of risks and uncertainty. We find compelling evidence for the advantages of entropy-based aggregation for forecasting inflation.

Keywords: model uncertainty; model aggregation; forecast combination; robust policy (search for similar items in EconPapers)
JEL-codes: C52 C53 E37 G11 (search for similar items in EconPapers)
Pages: 15
Date: 2025-10-09
New Economics Papers: this item is included in nep-mac
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Published in 2025

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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedawp:101967

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DOI: 10.29338/wp2025-12

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