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Combining forecast densities from VARs with uncertain instabilities
Author(s) -
Jore Anne Sofie,
Mitchell James,
Vahey Shaun P.
Publication year - 2010
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1162
Subject(s) - autoregressive model , econometrics , inflation (cosmology) , logarithm , vector autoregression , mathematics , computer science , statistics , mathematical analysis , physics , theoretical physics
Recursive‐weight forecast combination is often found to an ineffective method of improving point forecast accuracy in the presence of uncertain instabilities. We examine the effectiveness of this strategy for forecast densities using (many) vector autoregressive (VAR) and autoregressive (AR) models of output growth, inflation and interest rates. Our proposed recursive‐weight density combination strategy, based on the recursive logarithmic score of the forecast densities, produces well‐calibrated predictive densities for US real‐time data by giving substantial weight to models that allow for structural breaks. In contrast, equal‐weight combinations produce poorly calibrated forecast densities for Great Moderation data. Copyright © 2010 John Wiley & Sons, Ltd.