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Forecasting US inflation by Bayesian model averaging

  • Jonathan H. Wright

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

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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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
Contact details of provider: Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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