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Evaluating ensemble density combination - forecasting GDP and inflation

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Author Info

  • Karsten R. Gerdrup

    ()
    (Norges Bank (Central Bank of Norway))

  • Anne Sofie Jore

    ()
    (Norges Bank (Central Bank of Norway))

  • Christie Smith

    (Reserve Bank of New Zealand)

  • Leif Anders Thorsrud

    ()
    (Norges Bank (Central Bank of Norway))

Abstract

Forecast combination has become popular in central banks as a means to improve forecasts and to alleviate the risk of selecting poor models. However, if a model suite is populated with many similar models, then the weight attached to other independent models may be lower than warranted by their performance. One way to mitigate this problem is to group similar models into distinct `ensembles'. Using the original suite of models in Norges Bank's system for averaging models (SAM), we evaluate whether forecast performance can be improved by combining ensemble densities, rather than combining individual model densities directly. We evaluate performance both in terms of point forecasts and density forecasts, and test whether the densities are well-calibrated. We find encouraging results for combining ensembles.

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File URL: http://www.norges-bank.no/en/Published/Papers/Working-Papers/2009/WP-200919/
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Bibliographic Info

Paper provided by Norges Bank in its series Working Paper with number 2009/19.

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Length: 37 pages
Date of creation: 11 Nov 2009
Date of revision:
Handle: RePEc:bno:worpap:2009_19

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Related research

Keywords: forecasting; density combination; model combination; clustering; ensemble density; pits.;

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Cited by:
  1. Falch, Nina Skrove & Nymoen, Ragnar, 2011. "The accuracy of a forecast targeting central bank," Economics Discussion Papers 2011-6, Kiel Institute for the World Economy.
  2. Knut Are Aastveit & Karsten R. Gerdrup & Anne Sofie Jore & Leif Anders Thorsrud, 2011. "Nowcasting GDP in Real-Time: A Density Combination Approach," Working Papers 0003, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.

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