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Analyzing data revisions with a dynamic stochastic general equilibrium model

Author

Listed:
  • Dean Croushore
  • Keith Sill

Abstract

We use a structural dynamic stochastic general equilibrium model to investigate how initial data releases of key macroeconomic aggregates are related to final revised versions and how identified aggregate shocks influence data revisions. The analysis sheds light on how well preliminary data approximate final data and on how policy makers might condition their view of the preliminary data when formulating policy actions. The results suggest that monetary policy shocks and multifactor productivity shocks lead to predictable revisions to the initial release data on output growth and inflation.

Suggested Citation

  • Dean Croushore & Keith Sill, 2014. "Analyzing data revisions with a dynamic stochastic general equilibrium model," Working Papers 14-29, Federal Reserve Bank of Philadelphia.
    • Handle: RePEc:fip:fedpwp:14-29
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    References listed on IDEAS

    as
    1. Del Negro, Marco & Schorfheide, Frank, 2008. "Forming priors for DSGE models (and how it affects the assessment of nominal rigidities)," Journal of Monetary Economics, Elsevier, vol. 55(7), pages 1191-1208, October.
    2. N. Kundan Kishor & Evan F. Koenig, 2009. "VAR Estimation and Forecasting When Data Are Subject to Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 181-190, July.
    3. Del Negro, Marco & Schorfheide, Frank & Smets, Frank & Wouters, Rafael, 2007. "On the Fit of New Keynesian Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 25, pages 123-143, April.
    4. Lawrence J. Christiano & Martin Eichenbaum & Charles L. Evans, 2005. "Nominal Rigidities and the Dynamic Effects of a Shock to Monetary Policy," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 1-45, February.
    5. repec:taf:jnlbes:v:30:y:2012:i:2:p:181-190 is not listed on IDEAS
    6. Calvo, Guillermo A., 1983. "Staggered prices in a utility-maximizing framework," Journal of Monetary Economics, Elsevier, vol. 12(3), pages 383-398, September.
    7. Schorfheide, Frank & Sill, Keith & Kryshko, Maxym, 2010. "DSGE model-based forecasting of non-modelled variables," International Journal of Forecasting, Elsevier, vol. 26(2), pages 348-373, April.
    8. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    9. Sims, Christopher A, 2002. "Solving Linear Rational Expectations Models," Computational Economics, Springer;Society for Computational Economics, vol. 20(1-2), pages 1-20, October.
    10. Frank Smets & Raf Wouters, 2003. "An Estimated Dynamic Stochastic General Equilibrium Model of the Euro Area," Journal of the European Economic Association, MIT Press, vol. 1(5), pages 1123-1175, September.
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    Citations

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    Cited by:

    1. Amir-Ahmadi, Pooyan & Matthes, Christian & Wang, Mu-Chun, 2017. "Measurement errors and monetary policy: Then and now," Journal of Economic Dynamics and Control, Elsevier, vol. 79(C), pages 66-78.
    2. 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.
    3. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    4. 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.
    5. Santiago Pinto & Pierre-Daniel Sarte & Robert Sharp, 2020. "The Information Content and Statistical Properties of Diffusion Indexes," International Journal of Central Banking, International Journal of Central Banking, vol. 16(4), pages 47-99, September.

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    More about this item

    Keywords

    Real-time data; DSGE models; Bayesian analysis; Data revisions;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • 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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications

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