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Estimating time variation in measurement error from data revisions: an application to backcasting and forecasting in dynamic models
Author(s) -
Kapetanios George,
Yates Tony
Publication year - 2010
Publication title -
journal of applied econometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.878
H-Index - 99
eISSN - 1099-1255
pISSN - 0883-7252
DOI - 10.1002/jae.1121
Subject(s) - backcasting , econometrics , variance (accounting) , variation (astronomy) , observational error , aggregate (composite) , statistics , computer science , economics , mathematics , ecology , physics , materials science , accounting , astrophysics , sustainability , composite material , biology
Over time, economic statistics are refined. This implies that data measuring recent economic events are typically less reliable than older data. Such time variation in measurement error affects optimal forecasts. Measurement error, and its time variation, are of course unobserved. Our contribution is to show how estimates of these can be recovered from the variance of revisions to data using a behavioural model of the statistics agency. We illustrate the gains in forecasting performance from exploiting these estimates using a real‐time dataset on UK aggregate expenditure data. Copyright © 2009 John Wiley & Sons, Ltd.