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Combining forecast densities from VARs with uncertain instabilities

Author

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  • Anne Sofie Jore

    (Norges Bank, Oslo, Norway)

  • James Mitchell

    (National Institute of Economic and Social Research, London, UK)

  • Shaun P. Vahey

    (Australian National University, Canberra, Australia)

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.

Suggested Citation

  • Anne Sofie Jore & James Mitchell & Shaun P. Vahey, 2010. "Combining forecast densities from VARs with uncertain instabilities," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 621-634.
  • Handle: RePEc:jae:japmet:v:25:y:2010:i:4:p:621-634
    DOI: 10.1002/jae.1162
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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • F53 - International Economics - - International Relations, National Security, and International Political Economy - - - International Agreements and Observance; International Organizations
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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