This paper considers the problem of forecasting in large macroeconomic panels using\ud Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a\ud single model, are given. Practical methods for implementing Bayesian model averaging with factor models\ud are described. These methods involve algorithms which simulate from the space de…ned by all possible\ud models. We discuss how these simulation algorithms can also be used to select the model with the highest\ud marginal likelihood (or highest value of an information criterion) in an e¢cient manner. We apply these\ud methods to the problem of forecasting GDP and in‡ation using quarterly U.S. data on 162 time series.\ud For both GDP and in‡ation, we find that the models which contain factors do out-forecast an AR(p),\ud but only by a relatively small amount and only at short horizons. We attribute these findings to the\ud presence of structural instability and the fact that lags of dependent variable seem to contain most of the\ud information relevant for forecasting. Relative to the small forecasting gains provided by including factors,\ud the gains provided by using Bayesian model averaging over forecasting methods based on a single model\ud are appreciable
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