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Data Revisions in the Estimation of DSGE Models

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  • Casares, Miguel
  • Vázquez Pérez, Jesús

Abstract

Revisions of US macroeconomic data are not white-noise. They are persistent, correlated with real-time data, and with high variability (around 80% of volatility observed in US real-time data). Their business cycle effects are examined in an estimated DSGE model extended with both real-time and final data. After implementing a Bayesian estimation approach, the role of both habit formation and price indexation fall significantly in the extended model. The results show how revision shocks of both output and inflation are expansionary because they occur when real-time published data are too low and the Fed reacts by cutting interest rates. Consumption revisions, by contrast, are countercyclical as consumption habits mirror the observed reduction in real-time consumption. In turn, revisions of the three variables explain 9.3% of changes of output in its long-run variance decomposition.

Suggested Citation

  • Casares, Miguel & Vázquez Pérez, Jesús, 2012. "Data Revisions in the Estimation of DSGE Models," DFAEII Working Papers 2012-06, University of the Basque Country - Department of Foundations of Economic Analysis II.
  • Handle: RePEc:ehu:dfaeii:8759
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Data Revisions in the Estimation of DSGE Models
      by Christian Zimmermann in NEP-DGE blog on 2012-10-28 23:50:42
    2. Data Revisions in the Estimation of DSGE Models
      by Christian Zimmermann in NEP-DGE blog on 2012-10-28 23:50:42

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    Cited by:

    1. repec:eee:dyncon:v:82:y:2017:i:c:p:289-311 is not listed on IDEAS
    2. Jan Capek, 2014. "Historical Analysis of Monetary Policy Reaction Functions: Do Real-Time Data Matter?," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(6), pages 457-475, December.
    3. Martin Slanicay & Jan Čapek & Miroslav Hloušek, 2016. "Some Notes On Problematic Issues In Dsge Models," Economic Annals, Faculty of Economics, University of Belgrade, vol. 61(210), pages 79-100, July - Se.
    4. Milani, Fabio, 2017. "Sentiment and the U.S. business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 289-311.
    5. Capek Jan, 2015. "Estimating DSGE model parameters in a small open economy: Do real-time data matter?," Review of Economic Perspectives, Sciendo, vol. 15(1), pages 89-114, March.
    6. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    7. Pablo Aguilar & Jesús Vázquez, 2015. "The role of term structure in an estimated DSGE model with learning," Discussion Papers (IRES - Institut de Recherches Economiques et Sociales) 2015007, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).

    More about this item

    Keywords

    data revisions; DSGE models; business cycles;

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)

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