Forecasting in large macroeconomic panels using Bayesian Model Averaging
AbstractThis paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms that simulate from the space defined by all possible models. We explain how these simulation algorithms can also be used to select the model with the highest marginal likelihood (or highest value of an information criterion) in an efficient manner. We apply these methods to the problem of forecasting GDP and inflation using quarterly U.S. data on 162 time series. Our analysis indicates that models containing factors do outperform autoregressive models in forecasting both GDP and inflation, but only narrowly and at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of the dependent variable seem to contain most of the information relevant for forecasting.
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Bibliographic InfoPaper provided by Federal Reserve Bank of New York in its series Staff Reports with number 163.
Date of creation: 2003
Date of revision:
Other versions of this item:
- Gary Koop & Simon Potter, 2003. "Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging," Discussion Papers in Economics 04/16, Department of Economics, University of Leicester.
- NEP-ALL-2003-05-08 (All new papers)
- NEP-ECM-2003-05-15 (Econometrics)
- NEP-MAC-2003-05-08 (Macroeconomics)
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