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Data Uncertainty in General Equilibrium

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  • S. Boragan Aruoba

Abstract

In this paper, using recent empirical results regarding the statistical properties of macroeconomic data revisions, we study the effects of data revisions in a general equilibrium framework. We find that the presence of data revisions, or data uncertainty, creates a precautionary motive and causes significant changes in the decisions of agents. We also find that the model with revisions captures some aspects of the business cycle dynamics of the US data better than the benchmark model with no revisions. Using our model we measure the cost of having data revisions to be about $33 billion, $5 billion of which can be recovered by eliminating the predictability of revisions. Comparing these numbers with the budgets of the major statistical agencies in the US, we conclude that any money spent on the improvement of data collection would be well worth it

Suggested Citation

  • S. Boragan Aruoba, 2004. "Data Uncertainty in General Equilibrium," Computing in Economics and Finance 2004 131, Society for Computational Economics.
    • Handle: RePEc:sce:scecf4:131
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    References listed on IDEAS

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    1. Aruoba, S. Boragan & Fernandez-Villaverde, Jesus & Rubio-Ramirez, Juan F., 2006. "Comparing solution methods for dynamic equilibrium economies," Journal of Economic Dynamics and Control, Elsevier, vol. 30(12), pages 2477-2508, December.
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    7. Croushore, Dean & Stark, Tom, 2001. "A real-time data set for macroeconomists," Journal of Econometrics, Elsevier, vol. 105(1), pages 111-130, November.
    8. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    9. King, Robert G. & Rebelo, Sergio T., 1999. "Resuscitating real business cycles," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 14, pages 927-1007, Elsevier.
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    Cited by:

    1. Andrés González Gómez & Lavan Mahadeva & Diego Rodríguez & Luis Eduardo Rojas, 2009. "Monetary Policy Forecasting in a DSGE Model with Data that is Uncertain, Unbalanced and About the Future," Borradores de Economia 559, Banco de la Republica de Colombia.
    2. Katharina Glass, 2018. "Predictability of Euro Area Revisions," Macroeconomics and Finance Series 201801, University of Hamburg, Department of Socioeconomics.
    3. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    4. Jacobs, Jan P.A.M. & van Norden, Simon, 2016. "Why are initial estimates of productivity growth so unreliable?," Journal of Macroeconomics, Elsevier, vol. 47(PB), pages 200-213.
    5. Gregory E. Givens, 2017. "Do Data Revisions Matter for DSGE Estimation?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 49(6), pages 1385-1407, September.
    6. Seth Pruitt, 2012. "Uncertainty Over Models and Data: The Rise and Fall of American Inflation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44, pages 341-365, March.

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    More about this item

    Keywords

    Neoclassical growth model; productivity; forecasting; data uncertainty;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E13 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Neoclassical

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