Probability Forecast Combination via Entropy Regularized Wasserstein Distance
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.
File(s): File format is text/html https://philadelphiafed.org/-/media/research-and-data/publications/working-papers/2020/wp20-31r.pdf
Provider: Federal Reserve Bank of Philadelphia
Part of Series: Working Papers
Publication Date: 2020-08-06