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Forecast Accuracy and Economic Gains from Bayesian Model Averaging using Time Varying Weights

Listed author(s):
  • Lennart Hoogerheide

    (Erasmus University Rotterdam)

  • Richard Kleijn

    (PGGM, Zeist)

  • Francesco Ravazzolo

    (Norges Bank)

  • Herman K. van Dijk

    (Erasmus University Rotterdam)

  • Marno Verbeek

    (Erasmus University Rotterdam)

This discussion paper led to a publication in 'Journal of Forecasting' , 2010, 29(1-2), 251-269. 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.

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Paper provided by Tinbergen Institute in its series Tinbergen Institute Discussion Papers with number 09-061/4.

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Date of creation: 16 Jul 2009
Handle: RePEc:tin:wpaper:20090061
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