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

George Kapetanios

No 520, Working Papers from Queen Mary University of London, School of Economics and Finance

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.

Keywords: Forecasting; Data revisions (search for similar items in EconPapers)
JEL-codes: C32 C53 (search for similar items in EconPapers)
Date: 2004-10-01
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Citations: View citations in EconPapers (8)

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Working Paper: Estimating time-variation in measurement error from data revisions; an application to forecasting in dynamic models (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:qmw:qmwecw:520

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