Forecasting in large macroeconomic panels using Bayesian model averaging
By: Gary Koop and Simon Potter.
Contributors: Potter, Simon.; Federal Reserve Bank of New York.
Series: Staff reports ;; no. 163; Staff reports (Federal Reserve Bank of New York : Online) ;; no. 163.
Publisher: Federal Reserve Bank of New York
Published: 2003
Place of publication: [New York, N.Y.]
Language: eng
Format: Electronic resource
Subjects: Economic forecasting -- Econometric models.; Inflation (Finance) -- Forecasting -- Econometric models.
Description: "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"--Federal Reserve Bank of New York web site.
Notes: Includes bibliographical references.
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