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.

Bayesian model averaging, business cycle, forecast combination, portfolio optimization, time-varying model weights
Simulation Methods; Monte Carlo Methods; Bootstrap Methods (jel C15), Time-Series Models; Dynamic Quantile Regressions (jel C22), Econometric and Statistical Methods: Special Topics (jel C4), Forecasting and Other Model Applications (jel C53), Portfolio Choice; Investment Decisions (jel G11), Financial Forecasting (jel G17),
Econometric Institute Reprint Series
Journal of Forecasting
Erasmus Research Institute of Management

Hoogerheide, L.F, Kleijn, R.H, Ravazzolo, F, van Dijk, H.K, & Verbeek, M.J.C.M. (2010). Forecast accuracy and economic gains from Bayesian model averaging using time-varying weights. Journal of Forecasting, 29(1-2), 251–269. doi:10.1002/for.1145