Estimating Time-Variation in Measurement Error from Data Revisions: An Application to Forecasting in Dynamic Models
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.Download Info
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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.Length:
Date of creation: Oct 2004
Date of revision:
Handle: RePEc:qmw:qmwecw:wp520
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Related research
Keywords: Forecasting; Data revisions;Other versions of this item:
- George Kapetanios & Tony Yates, 2004. "Estimating time-variation in measurement error from data revisions; an application to forecasting in dynamic models," Bank of England working papers 238, Bank of England.
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
This paper has been announced in the following NEP Reports:
- NEP-ECM-2004-11-07 (Econometrics)
References
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Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.Cited by:
- Jarkko Jääskelä & Tony Yates, 2005. "Monetary policy and data uncertainty," Bank of England working papers 281, Bank of England.
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