Home About Latest Browse RSS Advanced Search

Board of Governors of the Federal Reserve System (U.S.)
International Finance Discussion Papers
Forecasting U.S. inflation by Bayesian Model Averaging
Jonathan H. Wright
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.


Download Full text
Download Full text
Cite this item
Jonathan H. Wright, Forecasting U.S. inflation by Bayesian Model Averaging, Board of Governors of the Federal Reserve System (U.S.), International Finance Discussion Papers 780, 2003.
More from this series
JEL Classification:
Subject headings:
Keywords: Inflation (Finance) ; Forecasting
For corrections, contact Ryan Wolfslayer ()
Fed-in-Print is the central catalog of publications within the Federal Reserve System. It is managed and hosted by the Economic Research Division, Federal Reserve Bank of St. Louis.

Privacy Legal