Accessibility navigation


Forecasting with vector autoregressive models of data vintages: US output growth and inflation

Clements, M. ORCID: https://orcid.org/0000-0001-6329-1341 and Galvao, A.B. (2013) Forecasting with vector autoregressive models of data vintages: US output growth and inflation. International Journal of Forecasting, 29 (4). pp. 698-714. ISSN 0169-2070

Full text not archived in this repository.

It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing.

To link to this item DOI: 10.1016/j.ijforecast.2011.09.003

Abstract/Summary

Vintage-based vector autoregressive models of a single macroeconomic variable are shown to be a useful vehicle for obtaining forecasts of different maturities of future and past observations, including estimates of post-revision values. The forecasting performance of models which include information on annual revisions is superior to that of models which only include the first two data releases. However, the empirical results indicate that a model which reflects the seasonal nature of data releases more closely does not offer much improvement over an unrestricted vintage-based model which includes three rounds of annual revisions.

Item Type:Article
Refereed:Yes
Divisions:Henley Business School > ICMA Centre
ID Code:35272
Uncontrolled Keywords:Data revisions; Forecasting; Data uncertainty
Publisher:Elsevier

University Staff: Request a correction | Centaur Editors: Update this record

Page navigation