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Data Revisions and DSGE Models

Ana Galvão ()

EMF Research Papers from Economic Modelling and Forecasting Group

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 macrovariables. The application also shows that the estimated relative importance of business cycle sources varies with data maturity.

Keywords: data revisions; medium-sized DSGE models; forecasting; variance decomposition JEL Classification Numbers: C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-dge, nep-ecm and nep-for
Date: 2016
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Journal Article: Data revisions and DSGE models (2017) Downloads
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