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Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models

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  • George Kapetanios

    (Queen Mary, University of London, London, UK)

  • Tony Yates

    (Bank of England, London, UK)

Abstract

Over time, economic statistics are refined. This implies that data measuring recent economic events are typically less reliable than older data. Such time variation in measurement error affects optimal forecasts. Measurement error, and its time variation, are of course unobserved. Our contribution is to show how estimates of these can be recovered from the variance of revisions to data using a behavioural model of the statistics agency. We illustrate the gains in forecasting performance from exploiting these estimates using a real-time dataset on UK aggregate expenditure data. Copyright © 2009 John Wiley & Sons, Ltd.

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Article provided by John Wiley & Sons, Ltd. in its journal Journal of Applied Econometrics.

Volume (Year): 25 (2010)
Issue (Month): 5 ()
Pages: 869-893

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Handle: RePEc:jae:japmet:v:25:y:2010:i:5:p:869-893

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  1. 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.
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Cited by:
  1. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
  2. Marek RUSNAK, 2013. "Revisions to the Czech National Accounts: Properties and Predictability," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 63(3), pages 244-261, July.

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