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Forecasting with measurement errors in dynamic models


  • Yates, Tony

    (Bank of England)

  • Richard Harrison
  • George Kapetanios


This paper explores the effects of measurement error on dynamic forecasting models. The paper sets out to illustrate a trade off that confronts forecasters and policymakers when they use data that are measured with error. On the one hand, observations on recent data give valuable clues as to the shocks that are hitting the system and will be propagated into the variables to be forecast (and which ultimately will inform monetary policy). But on the other, those recent observations are likely to be those least well measured. Two broad classes of results are illustrated. The first relates to cases where it is imagined that the forecaster takes the coefficients in the data generating process as a given, and has to choose how much of the historical time series of data to use to form a forecast. It is shown that if recent data is sufficiently badly measured, relative to older data, that it can be optimal in this case not to use old data at all. The second class of results is more general. Here, it is shown that for a general class of linear autoregressive forecasting models, the optimal weight to place on a data observation of some age, relative to the weight in the true data generating process, will depend on the measurement error in that data. The gains to be had in forecasting are illustrated using a model of UK business investment growth.

Suggested Citation

  • Yates, Tony & Richard Harrison & George Kapetanios, 2003. "Forecasting with measurement errors in dynamic models," Royal Economic Society Annual Conference 2003 225, Royal Economic Society.
  • Handle: RePEc:ecj:ac2003:225

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    References listed on IDEAS

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    Cited by:

    1. Alastair Cunningham & Jana Eklund & Chris Jeffery & George Kapetanios & Vincent Labhard, 2009. "A State Space Approach to Extracting the Signal From Uncertain Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(2), pages 173-180, March.
    2. Stekler, H.O., 2007. "The future of macroeconomic forecasting: Understanding the forecasting process," International Journal of Forecasting, Elsevier, vol. 23(2), pages 237-248.
    3. Andrea Silvestrini & Matteo Salto & Laurent Moulin & David Veredas, 2008. "Monitoring and forecasting annual public deficit every month: the case of France," Empirical Economics, Springer, vol. 34(3), pages 493-524, June.
    4. Fabio Busetti, 2006. "Preliminary data and econometric forecasting: an application with the Bank of Italy Quarterly Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(1), pages 1-23.
    5. George Kapetanios & Tony Yates, 2010. "Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(5), pages 869-893.
    6. Dean Croushore, 2011. "Frontiers of Real-Time Data Analysis," Journal of Economic Literature, American Economic Association, vol. 49(1), pages 72-100, March.
    7. Alastair Cunningham & Chris Jeffery & George Kapetanios & Vincent Labhard, 2007. "A State Space Approach To The Policymaker's Data Uncertainty Problem," Money Macro and Finance (MMF) Research Group Conference 2006 168, Money Macro and Finance Research Group.
    8. Juan Manuel Julio Román, 2011. "Modeling Data Revisions," BORRADORES DE ECONOMIA 007929, BANCO DE LA REPÚBLICA.
    9. Paul Downward & Andrew Mearman, 2005. "Methodological Triangulation at the Bank of England:An Investigation," Working Papers 0505, Department of Accounting, Economics and Finance, Bristol Business School, University of the West of England, Bristol.
    10. Emilia Tomczyk, 2013. "End of sample vs. real time data: perspectives for analysis of expectations," Working Papers 68, Department of Applied Econometrics, Warsaw School of Economics.
    11. Valentina Raponi & Cecilia Frale, 2014. "Revisions in official data and forecasting," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(3), pages 451-472, August.
    12. Paul Downward & Andrew Mearman, 2008. "Decision-making at the Bank of England: a critical appraisal," Oxford Economic Papers, Oxford University Press, vol. 60(3), pages 385-409, July.
    13. Clements Michael P., 2012. "Forecasting U.S. Output Growth with Non-Linear Models in the Presence of Data Uncertainty," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(1), pages 1-27, January.
    14. Cecilia Frale & Valentina Raponi, 2011. "Revisions in ocial data and forecasting," Working Papers LuissLab 1194, Dipartimento di Economia e Finanza, LUISS Guido Carli.
    15. Jarkko Jääskelä & Tony Yates, 2005. "Monetary policy and data uncertainty," Bank of England working papers 281, Bank of England.
    16. George Kapetanios & Tony Yates, 2004. "Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models," Working Papers 520, Queen Mary University of London, School of Economics and Finance.

    More about this item


    measurement error; forecasting; signal-extraction;

    JEL classification:

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

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