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Weights and pools for a Norwegian density combination

Author

Listed:
  • Hilde Bjørnland

    (Norwegian School of Management (BI) and Norges Bank (Central Bank of Norway))

  • Karsten Gerdrup

    (Norges Bank (Central Bank of Norway))

  • Christie Smith

    (Reserve Bank of New Zealand)

  • Anne Sofie Jore

    (Norges Bank (Central Bank of Norway))

  • Leif Anders Thorsrud

    (Norges Bank (Central Bank of Norway))

Abstract

We apply a suite of models to produce quasi-real-time density forecasts of Norwegian GDP and in ation, and evaluate dfferent combination and selection methods using the Kullback-Leibler information criterion (KLIC). We use linear and logarithmic opinion pools in conjunction with various weighting schemes, and we compare these combinations to two different selection methods. In our application, logarithmic opinion pools were better than linear opinion pools, and score-based weights were generally superior to other weighting schemes. Model selection generally yielded poor density forecasts, as evaluated by KLIC.

Suggested Citation

  • Hilde Bjørnland & Karsten Gerdrup & Christie Smith & Anne Sofie Jore & Leif Anders Thorsrud, 2010. "Weights and pools for a Norwegian density combination," Working Paper 2010/06, Norges Bank.
    • Handle: RePEc:bno:worpap:2010_06
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    References listed on IDEAS

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    Cited by:

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    2. Marcus P. A. Cobb, 2020. "Aggregate density forecasting from disaggregate components using Bayesian VARs," Empirical Economics, Springer, vol. 58(1), pages 287-312, January.
    3. McDonald, Christopher & Thamotheram, Craig & Vahey, Shaun P. & Wakerly, Elizabeth C., 2015. "Assessing the Economic Value of Probabilistic Forecasts in the Presence of an Inflation Target," EMF Research Papers 09, Economic Modelling and Forecasting Group.
    4. Knotek, Edward S. & Zaman, Saeed, 2023. "Real-time density nowcasts of US inflation: A model combination approach," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1736-1760.
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    6. Benjamin Avanzi & Yanfeng Li & Bernard Wong & Alan Xian, 2022. "Ensemble distributional forecasting for insurance loss reserving," Papers 2206.08541, arXiv.org, revised Jun 2024.
    7. Paulo M. Sánchez & Luis Fernando Melo, 2013. "Combinación de brechas del producto colombiano," Revista ESPE - Ensayos Sobre Política Económica, Banco de la República, vol. 31(72), pages 74-82, December.
    8. Nima Nonejad, 2021. "Crude oil price point forecasts of the Norwegian GDP growth rate," Empirical Economics, Springer, vol. 61(5), pages 2913-2930, November.
    9. Tommaso Proietti & Martyna Marczak & Gianluigi Mazzi, 2017. "Euromind‐ D : A Density Estimate of Monthly Gross Domestic Product for the Euro Area," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 32(3), pages 683-703, April.
    10. Boriss Siliverstovs, 2013. "Do business tendency surveys help in forecasting employment?: A real-time evidence for Switzerland," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2013(2), pages 129-151.
    11. Andres Trujillo-Barrera & Philip Garcia & Mindy L Mallory, 2018. "Short-term price density forecasts in the lean hog futures market," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(1), pages 121-142.
    12. Nalban, Valeriu, 2018. "Forecasting with DSGE models: What frictions are important?," Economic Modelling, Elsevier, vol. 68(C), pages 190-204.
    13. Garratt, Anthony & Mitchell, James & Vahey, Shaun P. & Wakerly, Elizabeth C., 2011. "Real-time inflation forecast densities from ensemble Phillips curves," The North American Journal of Economics and Finance, Elsevier, vol. 22(1), pages 77-87, January.
    14. Clark, Todd E. & Doh, Taeyoung, 2014. "Evaluating alternative models of trend inflation," International Journal of Forecasting, Elsevier, vol. 30(3), pages 426-448.
    15. Michelle Lewis, 2012. "Market Perceptions of Exchange Rate Risk," Reserve Bank of New Zealand Analytical Notes series AN2012/12, Reserve Bank of New Zealand.
    16. repec:bny:wpaper:0046 is not listed on IDEAS

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    More about this item

    Keywords

    Model combination; evaluation; density forecasting; KLIC;
    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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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