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

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Author Info
George Kapetanios () (Queen Mary, University of London)
Tony Yates (Bank of England)

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Abstract

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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Paper provided by Queen Mary, University of London, Department of Economics 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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Related research
Keywords: Forecasting; Data revisions;

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Find related papers by JEL classification:
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions
C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Other Model Applications

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References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
  1. Aoki, Kosuke, 2003. "On the optimal monetary policy response to noisy indicators," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 501-523, April. [Downloadable!] (restricted)
  2. Richard Harrison & George Kapetanios & Tony Yates, 2004. "Forecasting with Measurement Errors in Dynamic Models," Working Papers 521, Queen Mary, University of London, Department of Economics. [Downloadable!]
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  3. Eric T. Swanson, 2000. "On signal extraction and non-certainty-equivalence in optimal monetary policy rules," Finance and Economics Discussion Series 2000-32, Board of Governors of the Federal Reserve System (U.S.). [Downloadable!]
    Other versions:
  4. 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.
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  5. Andrew T. Levin & Gunter Coenen & Volker Wieland, 2001. "Data uncertainty and the role of money as an information variable for monetary policy," Working Paper Series 084, European Central Bank. [Downloadable!]
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  6. 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. [Downloadable!] (restricted)
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Cited by:
(explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.)

  1. Jarkko Jääskelä & Tony Yates, . "Monetary policy and data uncertainty," Bank of England working papers 281, Bank of England. [Downloadable!]
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