Economics at your fingertips  

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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (34) Track citations by RSS feed

Downloads: (external link) Link to full text; subscription required (text/html)

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

Access Statistics for this article

Journal of Forecasting is currently edited by Derek W. Bunn

More articles in Journal of Forecasting from John Wiley & Sons, Ltd.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing ().

Page updated 2019-04-19
Handle: RePEc:jof:jforec:v:28:y:2009:i:2:p:131-144