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Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models

Over time, economic statistics are refined. This means that newer data is typically less well measured than old data. Time variation in measurement error like this influences how forecasts should be made. We show how modelling the behaviour of the statistics agency generates both an estimate of this time variation and an estimate of the absolute amount of uncertainty in the data. We apply the method to UK aggregate expenditure data, and illustrate the gains in forecasting from exploiting our model estimates of measurement error.

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File URL: http://www.econ.qmul.ac.uk/papers/doc/wp520.pdf
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Paper provided by Queen Mary University of London, School of Economics and Finance in its series Working Papers with number 520.

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Date of creation: Oct 2004
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Handle: RePEc:qmw:qmwecw:wp520
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  1. 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.
  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. 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.).
  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. Aoki, Kosuke, 2003. "On the optimal monetary policy response to noisy indicators," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 501-523, April.
  6. 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.
  7. 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.
  8. Harrison, Richard & Kapetanios, George & Yates, Tony, 2005. "Forecasting with measurement errors in dynamic models," International Journal of Forecasting, Elsevier, vol. 21(3), pages 595-607.
  9. Nelson, Edward & Nikolov, Kalin, 2001. "UK Inflation in the 1970s and 1980s: The Role of Output Gap Mismeasurement," CEPR Discussion Papers 2999, C.E.P.R. Discussion Papers.
  10. 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.
  11. 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.
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