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Data Revisions in the Estimation of DSGE models

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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 that distinguishes real-time data from final data. Both the consumption habit formation and the price indexation to lagged inflation fall significantly in the estimation. The model also shows that 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. Finally, revisions of the three variables explain 9.3% of changes of output in its long-run variance decomposition.

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  • Miguel Casares & Jesús Vázquez, 2011. "Data Revisions in the Estimation of DSGE models," Documentos de Trabajo - Lan Gaiak Departamento de Economía - Universidad Pública de Navarra 1104, Departamento de Economía - Universidad Pública de Navarra.
    • Handle: RePEc:nav:ecupna:1104
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    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. Aguirre, Idoia & Vázquez, Jesús, 2018. "Inflation monitoring in real time: A comparative analysis of the Federal Reserve and the Bank of England," International Review of Economics & Finance, Elsevier, vol. 58(C), pages 200-209.
    2. Chou, Jenyu & Easaw, Joshy & Minford, Patrick, 2023. "Does inattentiveness matter for DSGE modeling? An empirical investigation," Economic Modelling, Elsevier, vol. 118(C).
    3. 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.
    4. Jenyu Chou & Yifei Cao & Patrick Minford, 2023. "Evaluation and indirect inference estimation of inattentive features in a New Keynesian framework," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(3), pages 530-542, April.
    5. Milani, Fabio, 2017. "Sentiment and the U.S. business cycle," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 289-311.
    6. Cardani, Roberta & Paccagnini, Alessia & Villa, Stefania, 2019. "Forecasting with instabilities: An application to DSGE models with financial frictions," Journal of Macroeconomics, Elsevier, vol. 61(C), pages 1-1.
    7. Pablo Aguilar & Jesús Vázquez, 2015. "The role of term structure in an estimated DSGE model with learning," LIDAM Discussion Papers IRES 2015007, Université catholique de Louvain, Institut de Recherches Economiques et Sociales (IRES).
    8. Aguilar, Pablo & Vázquez, Jesús, 2021. "An Estimated Dsge Model With Learning Based On Term Structure Information," Macroeconomic Dynamics, Cambridge University Press, vol. 25(7), pages 1635-1665, October.
    9. Steven P. Cassou & C. Patrick Scott & Jesús Vázquez, 2018. "Optimal monetary policy revisited: does considering US real-time data change things?," Applied Economics, Taylor & Francis Journals, vol. 50(57), pages 6203-6219, December.
    10. Roman Matkovskyy, 2019. "Extremal Economic (Inter)Dependence Studies: A Case of the Eastern European Countries," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(3), pages 667-698, September.
    11. repec:wrk:wrkemf:11 is not listed on IDEAS
    12. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    13. Funashima, Yoshito & Iizuka, Nobuo & Ohtsuka, Yoshihiro, 2020. "GDP announcements and stock prices," Journal of Economics and Business, Elsevier, vol. 108(C).
    14. Martin Slanicay & Jan Čapek & Miroslav Hloušek, 2016. "Some Notes On Problematic Issues In Dsge Models," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 61(210), pages 79-100, July - Se.
    15. 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.
    16. Pablo Aguilar & Jesús Vázquez, 2018. "Term structure and real-time learning," Working Papers 1803, Banco de España.

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    More about this item

    Keywords

    data revisions; DSGE models; business cycles.;
    All these keywords.

    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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