Forecasting in large macroeconomic panels using Bayesian Model Averaging

No 163, Staff Reports from Federal Reserve Bank of New York

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.

Keywords: Bayesian; forecasting; panel (search for similar items in EconPapers)
JEL-codes: C11 C53 E37 (search for similar items in EconPapers)
Date: 2003-03-01
New Economics Papers: this item is included in nep-ecm and nep-mac
Note: For a published version of this report, see Gary Koop and Simon Potter, "Forecasting in Dynamic Factor Models Using Bayesian Model Averaging," Econometrics Journal 7, no. 2 (2004): 550-65.
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Citations: View citations in EconPapers (37)

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Working Paper: Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging (2003)
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