Probability Forecast Combination via Entropy Regularized Wasserstein Distance

Ryan Cumings-Menon and Minchul Shin

No 20-31/R, Working Papers from Federal Reserve Bank of Philadelphia

Abstract: We propose probability and density forecast combination methods that are defined using the entropy regularized Wasserstein distance. First, we provide a theoretical characterization of the combined density forecast based on the regularized Wasserstein distance under the Gaus-sian assumption. Second, we show how this type of regularization can improve the predictive power of the resulting combined density. Third, we provide a method for choosing the tuning parameter that governs the strength of regularization. Lastly, we apply our proposed method to the U.S. inflation rate density forecasting, and illustrate how the entropy regularization can improve the quality of predictive density relative to its unregularized counterpart.

Keywords: Entropy regularization; Wasserstein distance; optimal transport; density forecasting; model combination (search for similar items in EconPapers)
JEL-codes: C53 E37 (search for similar items in EconPapers)
Pages: 26
Date: 2020-08-06
New Economics Papers: this item is included in nep-ecm, nep-for, nep-mac and nep-ore
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Persistent link: https://EconPapers.repec.org/RePEc:fip:fedpwp:88545

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DOI: 10.21799/frbp.wp.2020.31

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