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Estimating time-variation in measurement error from data revisions; an application to forecasting in dynamic models

  • George Kapetanios
  • Tony Yates

Over time, economic statistics are refined. This means that newer data are typically less well measured than old data. Time or vintage-variation in measurement error like this influences how forecasts should be made. Measurement error is obviously not directly observable. This paper shows that modelling the behaviour of the statistics agency generates an estimate of this time-variation. This provides an alternative to assuming that the final releases of variables are true. The paper applies the method to UK aggregate expenditure data, and demonstrates the gains in forecasting from exploiting these model-based estimates of measurement error.

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File URL: http://www.bankofengland.co.uk/research/Documents/workingpapers/2004/WP238.pdf
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Paper provided by Bank of England in its series Bank of England working papers with number 238.

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Date of creation: Nov 2004
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Handle: RePEc:boe:boeewp:238
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  1. Yates, Tony & Richard Harrison & George Kapetanios, 2003. "Forecasting with measurement errors in dynamic models," Royal Economic Society Annual Conference 2003 225, Royal Economic Society.
  2. Eric T. Swanson, 2000. "On signal extraction and non-certainty-equivalence in optimal monetary policy rules," Proceedings, Federal Reserve Bank of San Francisco.
  3. Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-63, July.
  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. Christina Gerberding & Franz Seitz & Andreas Worms, 2005. "How the Bundesbank really conducted monetary policy," Computing in Economics and Finance 2005 60, Society for Computational Economics.
  6. Swanson, Eric T., 2004. "Signal Extraction And Non-Certainty-Equivalence In Optimal Monetary Policy Rules," Macroeconomic Dynamics, Cambridge University Press, vol. 8(01), pages 27-50, February.
  7. Edward Nelson & Kalin Nikolov, 2001. "UK inflation in the 1970s and 1980s: the role of output gap mismeasurement," Bank of England working papers 148, Bank of England.
  8. Gunter Coenen & Andrew Levin & Volker Wieland, 2001. "Data uncertainty and the role of money as an information variable for monetary policy," Finance and Economics Discussion Series 2001-54, Board of Governors of the Federal Reserve System (U.S.).
  9. Howrey, E Philip, 1978. "The Use of Preliminary Data in Econometric Forecasting," The Review of Economics and Statistics, MIT Press, vol. 60(2), pages 193-200, May.
  10. Aoki, Kosuke, 2003. "On the optimal monetary policy response to noisy indicators," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 501-523, April.
  11. Sargent, Thomas J, 1989. "Two Models of Measurements and the Investment Accelerator," Journal of Political Economy, University of Chicago Press, vol. 97(2), pages 251-87, April.
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