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Forecasting US inflation by Bayesian model averaging

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  • Jonathan H. Wright

    (Department of Economics, Johns Hopkins University, Baltimore, MD, USA)

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.

Suggested Citation

  • Jonathan H. Wright, 2009. "Forecasting US inflation by Bayesian model averaging," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(2), pages 131-144.
  • Handle: RePEc:jof:jforec:v:28:y:2009:i:2:p:131-144
    DOI: 10.1002/for.1088
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    References listed on IDEAS

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