Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates

Abstract Bayesian methods for combining models and their forecasts are developed and applied using various time series models which yield forecasts of output growth rates for eighteen countries, 1974–87. Optimal Bayesian combining procedures involve use of posterior odds for alternative models which are derived and computed for several fixed parameter vs. time-varying parameter models. These odds shed light on the parameter constancy issue and are also used in predictive tests to decide whether or not combine forecasts of alternative models since, as indicated in the paper, it is not always optimal to combine forecasts. Finally, various Bayesian pooling techniques and non-Bayesian forecast combining techniques are described and their performance in forecasting one-year-ahead output growth rates of eighteen countries, 1974–87, is reported.

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