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Combining forecast densities from VARs with uncertain instabilities

Author

Listed:
  • Anne-Sofie Jore

    (Norges Bank (Central Bank of Norway))

  • James Mitchell

    (National Institute of Economic and Social Research (NIESR))

  • Shaun P. Vahey

    (Norges Bank (Central Bank of Norway) and Reserve Bank of New Zealand)

Abstract

Clark and McCracken (2008) argue that combining real-time point forecasts from VARs of output, prices and interest rates improves point forecast accuracy in the presence of uncertain model instabilities. In this paper, we generalize their approach to consider forecast density combinations and evaluations. Whereas Clark and McCracken (2008) show that the point forecast errors from particular equal-weight pairwise averages are typically comparable or better than benchmark univariate time series models, we show that neither approach produces accurate real-time forecast densities for recent US data. If greater weight is given to models that allow for the shifts in volatilities associated with the Great Moderation, predictive density accuracy improves substantially.

Suggested Citation

  • Anne-Sofie Jore & James Mitchell & Shaun P. Vahey, 2008. "Combining forecast densities from VARs with uncertain instabilities," Working Paper 2008/01, Norges Bank.
  • Handle: RePEc:bno:worpap:2008_01
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    References listed on IDEAS

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    More about this item

    Keywords

    Density forecasts; Uncertainty; Combining forecasts; Evaluating forcasts; VAR models;
    All these keywords.

    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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