Forecasting U.S. inflation by Bayesian Model Averaging

Jonathan Wright

No 780, International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.)

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: Inflation (Finance); Forecasting (search for similar items in EconPapers)
Date: 2003
New Economics Papers: this item is included in nep-cba, nep-ecm, nep-ets and nep-mon
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (19)

Downloads: (external link)
http://www.federalreserve.gov/pubs/ifdp/2003/780/default.htm (text/html)
http://www.federalreserve.gov/pubs/ifdp/2003/780/ifdp780.pdf (application/pdf)

Related works:
Journal Article: Forecasting US inflation by Bayesian model averaging (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fedgif:780

Access Statistics for this paper

More papers in International Finance Discussion Papers from Board of Governors of the Federal Reserve System (U.S.) Contact information at EDIRC.
Bibliographic data for series maintained by Ryan Wolfslayer ; Keisha Fournillier ().