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Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights

Author

Listed:
  • Lennart Hoogerheide

    (Econometric and Tinbergen Institutes, Erasmus University Rotterdam, The Netherlands)

  • Richard Kleijn

    (PGGM, Zeist, The Netherlands)

  • Francesco Ravazzolo

    (Norges Bank, Oslo, Norway)

  • Herman K. Van Dijk

    (Econometric and Tinbergen Institutes, Erasmus University Rotterdam, The Netherlands)

  • Marno Verbeek

    (Rotterdam School of Management, Erasmus University, Rotterdam, The Netherlands)

Abstract
Several Bayesian model combination schemes, including some novel approaches that simultaneously allow for parameter uncertainty, model uncertainty and robust time-varying model weights, are compared in terms of forecast accuracy and economic gains using financial and macroeconomic time series. The results indicate that the proposed time-varying model weight schemes outperform other combination schemes in terms of predictive and economic gains. In an empirical application using returns on the S&P 500 index, time-varying model weights provide improved forecasts with substantial economic gains in an investment strategy including transaction costs. Another empirical example refers to forecasting US economic growth over the business cycle. It suggests that time-varying combination schemes may be very useful in business cycle analysis and forecasting, as these may provide an early indicator for recessions. Copyright © 2009 John Wiley & Sons, Ltd.

Suggested Citation

  • Lennart Hoogerheide & Richard Kleijn & Francesco Ravazzolo & Herman K. Van Dijk & Marno Verbeek, 2010. "Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 251-269.
    • Handle: RePEc:jof:jforec:v:29:y:2010:i:1-2:p:251-269
    DOI: 10.1002/for.1145
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    More about this item

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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