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| Title: | | Forecasting in large macroeconomic panels using Bayesian model averaging  |
| Authors: | | Koop, Gary Potter, Simon |
| Issue Date: | | 2003 |
| Series/Report no.: | | Staff Report, Federal Reserve Bank of New York 163 |
| Abstract: | | This 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. |
| Subjects: | | Bayesian, forecasting, panel |
| JEL: | | C11 C53 E37 |
| Document Type: | | Working Paper |
| Appears in Collections: | | Staff Reports, Federal Reserve Bank of New York
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