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Data revisions and DSGE models

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Galvão, Ana Beatriz (2017) Data revisions and DSGE models. Journal of Econometrics, 196 (1). pp. 2015-232. doi:10.1016/j.jeconom.2016.09.006

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Official URL: https://doi.org/10.1016/j.jeconom.2016.09.006

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Abstract

The typical estimation of DSGE models requires data on a set of macroeconomic aggregates, such as output, consumption and investment, which are subject to data revisions. The conventional approach employs the time series that is currently available for these aggregates for estimation, implying that the last observations are still subject to many rounds of revisions. This paper proposes a release-based approach that uses revised data of all observations to estimate DSGE models, but the model is still helpful for real-time forecasting. This new approach accounts for data uncertainty when predicting future values of macroeconomic variables subject to revisions, thus providing policy-makers and professional forecasters with both backcasts and forecasts. Application of this new approach to a medium-sized DSGE model improves the accuracy of density forecasts, particularly the coverage of predictive intervals, of US real macro variables. The application also shows that the estimated relative importance of business cycle sources varies with data maturity.

Item Type: Journal Article
Divisions: Faculty of Social Sciences > Warwick Business School > Global Energy
Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Macroeconomics, Macroeconomics--Mathematical models, Econometrics
Journal or Publication Title: Journal of Econometrics
Publisher: Elsevier
ISSN: 0304-4076
Official Date: January 2017
Dates:
DateEvent
January 2017Published
11 October 2016Available
12 September 2016Accepted
Volume: 196
Number: 1
Page Range: pp. 2015-232
DOI: 10.1016/j.jeconom.2016.09.006
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Funder: ESRC
Grant number: ES/KS10611/1
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