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Forecasting US inflation by Bayesian model averaging

Jonathan Wright

Journal of Forecasting, 2009, vol. 28, issue 2, 131-144

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.

Date: 2009
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Working Paper: Forecasting U.S. inflation by Bayesian Model Averaging (2003) Downloads
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