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Improving Real-Time Estimates of Output and Inflation Gaps With Multiple-Vintage Models

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  • Michael P. Clements
  • Ana Beatriz Galvão

Abstract

Real-time estimates of output gaps and inflation gaps differ from the values that are obtained using data available long after the event. Part of the problem is that the data on which the real-time estimates are based is subsequently revised. We show that vector-autoregressive models of data vintages provide forecasts of post-revision values of future observations and of already-released observations capable of improving estimates of output and inflation gaps in real time. Our findings indicate that annual revisions to output and inflation data are in part predictable based on their past vintages. This article has online supplementary materials.

Suggested Citation

  • Michael P. Clements & Ana Beatriz Galvão, 2012. "Improving Real-Time Estimates of Output and Inflation Gaps With Multiple-Vintage Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(4), pages 554-562, May.
    • Handle: RePEc:taf:jnlbes:v:30:y:2012:i:4:p:554-562
    DOI: 10.1080/07350015.2012.707588
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    References listed on IDEAS

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    1. Dean Croushore, 2019. "Revisions to PCE Inflation Measures: Implications for Monetary Policy," International Journal of Central Banking, International Journal of Central Banking, vol. 15(4), pages 241-265, October.
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    Cited by:

    1. Adam J. Check & Anna K Nolan & Tyler C. Schipper, 2019. "Forecasting GDP Growth using Disaggregated GDP Revisions," Economics Bulletin, AccessEcon, vol. 39(4), pages 2580-2588.
    2. Chalmovianský, Jakub & Němec, Daniel, 2022. "Assessing uncertainty of output gap estimates: Evidence from Visegrad countries," Economic Modelling, Elsevier, vol. 116(C).
    3. Galimberti, Jaqueson K. & Moura, Marcelo L., 2016. "Improving the reliability of real-time output gap estimates using survey forecasts," International Journal of Forecasting, Elsevier, vol. 32(2), pages 358-373.
    4. Ana Beatriz Galvão & James Mitchell, 2019. "Measuring Data Uncertainty: An Application using the Bank of England's "Fan Charts" for Historical GDP Growth," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers ESCoE DP-2019-08, Economic Statistics Centre of Excellence (ESCoE).
    5. Jens Boysen‐Hogrefe, 2015. "Monetary Aggregates to Improve Early Output Gap Estimates in the Euro Area: An Empirical Assessment," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 533-542, November.
    6. Clements, Michael P., 2016. "Long-run restrictions and survey forecasts of output, consumption and investment," International Journal of Forecasting, Elsevier, vol. 32(3), pages 614-628.
    7. Clements, Michael P., 2016. "Real-time factor model forecasting and the effects of instability," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 661-675.
    8. repec:wrk:wrkemf:35 is not listed on IDEAS
    9. repec:wrk:wrkemf:24 is not listed on IDEAS
    10. Carriero, Andrea & Clements, Michael P. & Galvão, Ana Beatriz, 2015. "Forecasting with Bayesian multivariate vintage-based VARs," International Journal of Forecasting, Elsevier, vol. 31(3), pages 757-768.
    11. Michael P. Clements, 2017. "Assessing Macro Uncertainty in Real-Time When Data Are Subject To Revision," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(3), pages 420-433, July.
    12. Michael P. Clements, 2014. "Anticipating Early Data Revisions to US GDP and the Effects of Releases on Equity Markets," ICMA Centre Discussion Papers in Finance icma-dp2014-06, Henley Business School, University of Reading.

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