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

Lennart Hoogerheide (), Richard Kleijn (), Francesco Ravazzolo (), Herman K. van Dijk and Marno Verbeek ()
Additional contact information
Lennart Hoogerheide: Erasmus University Rotterdam
Richard Kleijn: PGGM, Zeist
Marno Verbeek: Erasmus University Rotterdam

No 09-061/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

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.

Keywords: forecast combination; Bayesian model averaging; time varying model weights; portfolio optimization; business cycle (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 C53 G11 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-mac and nep-ore
Date: 2009-07-16

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Related works:
Working Paper: Forecast accuracy and economic gains from Bayesian model averaging using time varying weight (2009)
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Persistent link: http://EconPapers.repec.org/RePEc:dgr:uvatin:20090061

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