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Data revisions and DSGE models

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  • Galvão, Ana Beatriz

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.

Suggested Citation

  • Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
  • Handle: RePEc:eee:econom:v:196:y:2017:i:1:p:215-232
    DOI: 10.1016/j.jeconom.2016.09.006
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    1. Data Revisions and DSGE Models
      by Christian Zimmermann in NEP-DGE blog on 2017-03-24 02:00:05

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    More about this item

    Keywords

    Data revisions; Medium-sized DSGE models; Forecasting; Variance decomposition;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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