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Assessing Macro Uncertainty In Real-Time When Data Are Subject To Revision

Author

Listed:
  • Michael P. Clements

    (ICMA Centre, Henley Business School, University of Reading)

Abstract
Model-based estimates of future uncertainty are generally based on the in-sample fit of the model, as when Box-Jenkins prediction intervals are calculated. However, this approach will generate biased uncertainty estimates in real time when there are data revisions. A simple remedy is suggested, and used to generate more accurate prediction intervals for 25 macroeconomic variables, in line with the theory. A simulation study based on an empirically-estimated model of data revisions for US output growth is used to investigate small-sample properties.

Suggested Citation

  • Michael P. Clements, 2015. "Assessing Macro Uncertainty In Real-Time When Data Are Subject To Revision," ICMA Centre Discussion Papers in Finance icma-dp2015-02, Henley Business School, University of Reading.
    • Handle: RePEc:rdg:icmadp:icma-dp2015-02
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    References listed on IDEAS

    as
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    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. repec:wrk:wrkemf:11 is not listed on IDEAS
    2. Galvão, Ana Beatriz, 2017. "Data revisions and DSGE models," Journal of Econometrics, Elsevier, vol. 196(1), pages 215-232.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Zhang, Bo & Nguyen, Bao H., 2020. "Real-time forecasting of the Australian macroeconomy using Bayesian VARs," Working Papers 2020-12, University of Tasmania, Tasmanian School of Business and Economics.
    5. Chenghan Hou & Bao Nguyen & Bo Zhang, 2023. "Real‐time forecasting of the Australian macroeconomy using flexible Bayesian VARs," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 418-451, March.
    6. Michael P. Clements & Ana Beatriz Galvão, 2023. "Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(2), pages 164-185, March.
    7. Baetje, Fabian & Friedrici, Karola, 2016. "Does cross-sectional forecast dispersion proxy for macroeconomic uncertainty? New empirical evidence," Economics Letters, Elsevier, vol. 143(C), pages 38-43.
    8. Galvão, Ana Beatriz & Lopresto, Marta, 2020. "Real-Time Probabilistic Nowcasts Of Uk Quarterly Gdp Growth Using A Mixed-Frequency Bottom-Up Approach," National Institute Economic Review, National Institute of Economic and Social Research, vol. 254, pages 1-11, November.
    9. Michael P Clements & Ana Beatriz Galvao, 2017. "Data Revisions and Real-time Probabilistic Forecasting of Macroeconomic Variables," ICMA Centre Discussion Papers in Finance icma-dp2017-01, Henley Business School, University of Reading.
    10. Graziano Moramarco, 2022. "Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers," Econometrics, MDPI, vol. 11(1), pages 1-29, December.
    11. David Alan Peel & Pantelis Promponas, 2016. "Forecasting the nominal exchange rate movements in a changing world. The case of the U.S. and the U.K," Working Papers 144439514, Lancaster University Management School, Economics Department.
    12. repec:wrk:wrkemf:36 is not listed on IDEAS
    13. Graziano Moramarco, 2020. "Measuring Global Macroeconomic Uncertainty," Working Papers wp1148, Dipartimento Scienze Economiche, Universita' di Bologna.

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    More about this item

    Keywords

    in-sample uncertainty; out-of-sample uncertainty; real-time-vintage estimation;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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