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Combining forecast densities from VARs with uncertain instabilities

✍ Scribed by Anne Sofie Jore; James Mitchell; Shaun P. Vahey

Publisher: John Wiley and Sons
Year: 2010
Tongue: English
Weight: 122 KB
Volume: 25
Category: Article
ISSN: 0883-7252
DOI: 10.1002/jae.1162

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✦ Synopsis

Abstract

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.

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## Abstract Recent work suggests VAR models of output, inflation, and interest rates may be prone to instabilities. In the face of such instabilities, a variety of estimation or forecasting methods might be used to improve the accuracy of forecasts from a VAR. The uncertainty inherent in any single