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Forecasting US inflation by Bayesian model averaging

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Author Info
Jonathan H. Wright (Department of Economics, Johns Hopkins University, Baltimore, MD, USA)

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Abstract

Recent empirical work has considered the prediction of inflation by combining the information in a large number of time series. One such method that has been found to give consistently good results consists of simple equal-weighted averaging of the forecasts from a large number of different models, each of which is a linear regression relating inflation to a single predictor and a lagged dependent variable. In this paper, I consider using Bayesian model averaging for pseudo out-of-sample prediction of US inflation, and find that it generally gives more accurate forecasts than simple equal-weighted averaging. This superior performance is consistent across subsamples and a number of inflation measures. Copyright © 2008 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/for.1088
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Publisher Info
Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

Volume (Year): 28 (2009)
Issue (Month): 2 ()
Pages: 131-144
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Handle: RePEc:jof:jforec:v:28:y:2009:i:2:p:131-144

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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  2. 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. [Downloadable!]
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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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  7. Stock, James H. & Watson, Mark W., 1999. "Forecasting inflation," Journal of Monetary Economics, Elsevier, vol. 44(2), pages 293-335, October. [Downloadable!] (restricted)
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  8. James H. Stock & Mark W. Watson, 2001. "Forecasting output and inflation: the role of asset prices," Proceedings, Federal Reserve Bank of San Francisco, issue Mar. [Downloadable!]
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  9. Richard Clarida & Jordi Galí & Mark Gertler, 2000. "Monetary Policy Rules And Macroeconomic Stability: Evidence And Some Theory," The Quarterly Journal of Economics, MIT Press, vol. 115(1), pages 147-180, February. [Downloadable!] (restricted)
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  10. K. J. Martijn Cremers, 2002. "Stock Return Predictability: A Bayesian Model Selection Perspective," Review of Financial Studies, Oxford University Press for Society for Financial Studies, vol. 15(4), pages 1223-1249.
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  13. 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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  14. Christopher A. Sims, 2002. "The Role of Models and Probabilities in the Monetary Policy Process," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2002-2), pages 1-62. [Downloadable!]
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  1. William Brock & Steven Durlauf & Kenneth West, 2005. "Model uncertainty and policy evaluation: some theory and empirics," Proceedings, Federal Reserve Bank of San Francisco. [Downloadable!]
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  2. Chun Liu & John M. Maheu, 2009. "Forecasting realized volatility: a Bayesian model-averaging approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 24(5), pages 709-733. [Downloadable!]
    Other versions:
  3. Diego Nocetti & William T. Smith, 2006. "Why Do Pooled Forecasts Do Better Than Individual Forecasts Ex Post?," Economics Bulletin, Economics Bulletin, vol. 4(36), pages 1-7. [Downloadable!]
  4. 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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  5. 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)
  6. repec:att:wimass:1920419 is not listed on IDEAS
  7. 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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  8. John M Maheu & Thomas H McCurdy, 2007. "How useful are historical data for forecasting the long-run equity return distribution?," Working Papers tecipa-293, University of Toronto, Department of Economics. [Downloadable!]
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  9. 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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  10. Jan J. J. Groen & George Kapetanios, 2008. "Revisiting useful approaches to data-rich macroeconomic forecasting," Staff Reports 327, Federal Reserve Bank of New York. [Downloadable!]
    Other versions:
  11. Stefania D'Amico, 2005. "Density selection and combination under model ambiguity: an application to stock returns," Finance and Economics Discussion Series 2005-09, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
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