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Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models Author info | Abstract | Publisher info | Download info | Related research | Statistics George Kapetanios () (Queen Mary, University of London)
Tony Yates (Bank of England)
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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 2004Date of revision:
Handle: RePEc:qmw:qmwecw:wp520Contact details of provider: Postal: London E1 4NS Phone: +44 (0) 20 7882 5096 Fax: +44 (0) 20 8983 3580 Web page: http://www.econ.qmul.ac.uk More information through EDIRC
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Keywords: Forecasting ; Data revisions ; Other versions of this item:
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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Jarkko Jääskelä & Tony Yates, .
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Bank of England working papers
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