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Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts

Author

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  • Christopher G. Gibbs

    (School of Economics, UNSW Business School, UNSW)

  • Andrey L. Vasnev

    (University of Sydney)

Abstract

In applied forecasting, there is a trade-off between in-sample fit and out-of-sample forecast accuracy. Parsimonious model specifications typically outperform richer model specifications. Consequently, there is often predictable information in forecast errors that is difficult to exploit. However, we show how this predictable information can be exploited in forecast combinations. In this case, optimal combination weights should minimize conditional mean squared error, or a conditional loss function, rather than the unconditional variance as in the commonly used framework of Bates and Granger (1969). We prove that our conditionally optimal weights lead to better forecast performance. The conditionally optimal weights support other forward-looking approaches to combining forecasts, where the forecast weights depend on the expected model performance. We show that forward-looking

Suggested Citation

  • Christopher G. Gibbs & Andrey L. Vasnev, 2017. "Conditionally Optimal Weights and Forward-Looking Approaches to Combining Forecasts," Discussion Papers 2017-10, School of Economics, The University of New South Wales.
  • Handle: RePEc:swe:wpaper:2017-10
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    File URL: http://research.economics.unsw.edu.au/RePEc/papers/2017-10.pdf
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    Cited by:

    1. Granziera, Eleonora & Sekhposyan, Tatevik, 2019. "Predicting relative forecasting performance: An empirical investigation," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1636-1657.
    2. Fossati, Sebastian, 2017. "Testing for State-Dependent Predictive Ability," Working Papers 2017-9, University of Alberta, Department of Economics.
    3. Wei Qian & Craig A. Rolling & Gang Cheng & Yuhong Yang, 2019. "On the Forecast Combination Puzzle," Econometrics, MDPI, Open Access Journal, vol. 7(3), pages 1-26, September.
    4. Radchenko, Peter & Vasnev, Andrey & Wang, Wendun, 2020. "Too similar to combine? On negative weights in forecast combination," Working Papers BAWP-2020-02, University of Sydney Business School, Discipline of Business Analytics.

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    More about this item

    Keywords

    Forecast combination; conditionally optimal weights; forecast combination puzzle; inflation; Phillips curve;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation

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