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Modelling the data measurement process for the index of production

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  • K. D. Patterson

Abstract

Many statistical series that are available from official agencies, such as the Office for National Statistics in the UK and the Bureau of Economic Analysis in the USA, are subject to an extensive process of revision and refinement. This feature of the data is often not explicitly recognized by users even though it may be important to their use of the data. The starting‐point of this study is to conceptualize and model the data measurement process as it is relevant to the index of production (IOP). The IOP attracts considerable attention because of its timely publication and importance as an indicator of the UK's industrial base. This study shows that there is one common stochastic trend (and one common factor in terms of observable variables) `driving' 13 vintages of data on the IOP. Necessary and sufficient conditions are derived for the `final' vintage of data on the IOP to be the permanent component of the series in the Gonzalo–Granger sense, and the revisions to be the transitory components. These conditions are not satisfied for the IOP; hence, the per‐manent component is a function of all the published vintages.

Suggested Citation

  • K. D. Patterson, 2002. "Modelling the data measurement process for the index of production," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 279-296, June.
    • Handle: RePEc:bla:jorssa:v:165:y:2002:i:2:p:279-296
    DOI: 10.1111/1467-985X.00622
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    1. Hecq, Alain & Jacobs, Jan P.A.M. & Stamatogiannis, Michalis P., 2019. "Testing for news and noise in non-stationary time series subject to multiple historical revisions," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 396-407.
    2. Jacobs, Jan P.A.M. & van Norden, Simon, 2011. "Modeling data revisions: Measurement error and dynamics of "true" values," Journal of Econometrics, Elsevier, vol. 161(2), pages 101-109, April.
    3. Oller, Lars-Erik & Teterukovsky, Alex, 2007. "Quantifying the quality of macroeconomic variables," International Journal of Forecasting, Elsevier, vol. 23(2), pages 205-217.

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