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Forecasting in Large Macroeconomic Panels using Bayesian Model Averaging

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Gary Koop ()
Simon Potter

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Abstract

This paper considers the problem of forecasting in large macroeconomic panels using Bayesian model averaging. Theoretical justifications for averaging across models, as opposed to selecting a single model, are given. Practical methods for implementing Bayesian model averaging with factor models are described. These methods involve algorithms which simulate from the space defined by all possible models. We discuss 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. For both GDP and inflation, we find that the models which contain factors do out-forecast an AR(p), but only by a relatively small amount and only at short horizons. We attribute these findings to the presence of structural instability and the fact that lags of dependent variable seem to contain most of the information relevant for forecasting. Relative to the small forecasting gains provided by including factors, the gains provided by using Bayesian model averaging over forecasting methods based on a single model are appreciable.

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Paper provided by Department of Economics, University of Leicester in its series Discussion Papers in Economics with number 04/16.

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Date of creation: Jan 2003
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Handle: RePEc:lec:leecon:04/16

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  1. James H. Stock & Mark W. Watson, 2002. "Has the Business Cycle Changed and Why?," NBER Working Papers 9127, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  2. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2004. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," NBER Working Papers 10220, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  3. Carmen Fernandez & Eduardo Ley & Mark Steel, 1999. "Model uncertainty in cross-country growth regressions," Econometrics 9903003, EconWPA, revised 06 Oct 2001. [Downloadable!]
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  4. Fernandez, Carmen & Ley, Eduardo & Steel, Mark F. J., 2001. "Benchmark priors for Bayesian model averaging," Journal of Econometrics, Elsevier, vol. 100(2), pages 381-427, February. [Downloadable!] (restricted)
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  5. Jean Boivin & Serena Ng, 2003. "Are More Data Always Better for Factor Analysis?," NBER Working Papers 9829, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  6. Raffaella Giacomini & Halbert White, 2003. "Tests of Conditional Predictive Ability," Econometrics 0308001, EconWPA. [Downloadable!]
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  7. Mario Forni & Marc Hallin & Marco Lippi & Lucrezia Reichlin, 2000. "The Generalized Dynamic-Factor Model: Identification And Estimation," The Review of Economics and Statistics, MIT Press, vol. 82(4), pages 540-554, November. [Downloadable!] (restricted)
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  8. Min, Chung-ki & Zellner, Arnold, 1993. "Bayesian and non-Bayesian methods for combining models and forecasts with applications to forecasting international growth rates," Journal of Econometrics, Elsevier, vol. 56(1-2), pages 89-118, March. [Downloadable!] (restricted)
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  9. Stock, James H & Watson, Mark W, 2002. "Macroeconomic Forecasting Using Diffusion Indexes," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(2), pages 147-62, April.
  10. Giannone, Domenico & Reichlin, Lucrezia & Sala, Luca, 2002. "Tracking Greenspan: Systematic and Unsystematic Monetary Policy Revisited," CEPR Discussion Papers 3550, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  11. Thomas Knox & James H. Stock & Mark W. Watson, 2000. "Empirical Bayes Forecasts of One Time Series Using Many Predictors," Econometric Society World Congress 2000 Contributed Papers 1421, Econometric Society. [Downloadable!]
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  12. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January. [Downloadable!] (restricted)
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  1. Todd E. Clark & Michael W. McCracken, 2004. "Improving forecast accuracy by combining recursive and rolling forecasts," Research Working Paper RWP 04-10, Federal Reserve Bank of Kansas City. [Downloadable!]
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  2. Katja Drechsel & Laurent Maurin, 2008. "Flow on conjunctural information and forecast of euro area economic activity," Working Paper Series 925, European Central Bank. [Downloadable!]
  3. George Kapetanios & Vincent Labhard & Simon Price, . "Forecast combination and the Bank of England’s suite of statistical forecasting models," Bank of England working papers 323, Bank of England. [Downloadable!]
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  4. Jonathan H. Wright, 2003. "Bayesian Model Averaging and exchange rate forecasts," International Finance Discussion Papers 779, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  5. Jonathan H. Wright, 2003. "Forecasting U.S. inflation by Bayesian Model Averaging," International Finance Discussion Papers 780, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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  6. Jon Faust & Jonathan H. Wright, 2007. "Comparing Greenbook and Reduced Form Forecasts using a Large Realtime Dataset," NBER Working Papers 13397, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  7. Christine De Mol & Domenico Giannone & Lucrezia Reichlin, 2006. "Forecasting using a large number of predictors - Is Bayesian regression a valid alternative to principal components?," Working Paper Series 700, European Central Bank. [Downloadable!]
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  8. Inoue, Atsushi & Kilian, Lutz, 2005. "How Useful is Bagging in Forecasting Economic Time Series? A Case Study of US CPI Inflation," CEPR Discussion Papers 5304, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  9. Todd E. Clark & Michael W. McCracken, 2006. "Forecasting of small macroeconomic VARs in the presence of instabilities," Research Working Paper RWP 06-09, Federal Reserve Bank of Kansas City. [Downloadable!]
  10. George Kapetanios & Vincent Labhard & Simon Price, . "Forecasting using Bayesian and information theoretic model averaging: an application to UK inflation," Bank of England working papers 268, Bank of England. [Downloadable!]
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  11. John W. Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Working Papers 07-1, Bank of Canada. [Downloadable!]
  12. Garett Jones & W. Joel Schneider, 2005. "Intelligence, Human Capital, and Economic Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," Development and Comp Systems 0507005, EconWPA. [Downloadable!]
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  13. Joshua Gallin & Randal Verbrugge, 2007. "Improving the CPI’s Age-Bias Adjustment: Leverage, Disaggregation and Model Averaging," Working Papers 411, U.S. Bureau of Labor Statistics. [Downloadable!]
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