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Forecasting U.S. Inflation by Bayesian Model Averaging

33 Pages Posted: 22 Nov 2003  

Jonathan H. Wright

Johns Hopkins University - Department of Economics

Date Written: September 2003

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 over a large number of different models, each of which is a linear regression model that relates 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 gives more accurate forecasts than simple equal weighted averaging. This superior performance is consistent across subsamples and inflation measures. Meanwhile, both methods substantially outperform a naive time series benchmark of predicting inflation by an autoregression.

Keywords: shrinkage, Phillips curve, model uncertainty, forecasting, inflation

JEL Classification: C32, C53, E31, E37

Suggested Citation

Wright, Jonathan H., Forecasting U.S. Inflation by Bayesian Model Averaging (September 2003). FRB International Finance Discussion Paper No. 780. Available at SSRN: https://ssrn.com/abstract=457360 or http://dx.doi.org/10.2139/ssrn.457360

Jonathan H. Wright (Contact Author)

Johns Hopkins University - Department of Economics ( email )

3400 Charles Street
Baltimore, MD 21218-2685
United States

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