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Methodological aspects of time series back-calculation

Author

Listed:
  • Massimiliano Caporin

    (Department of Economics, Universit� di Padova)

  • Domenico Sartore

    (Department of Economics, University Of Venice Ca� Foscari)

Abstract

This paper provides the theoretical and operational framework for estimating past values of relevant time series starting from a (limited) information set. We consider a general approach that includes as special cases time series aggregation and temporal and/or spatial disaggregation problems. Furthermore, we explore the relevant problems and the possible solutions associated with a retropolation exercise, evidencing that linear models could be the preferred representation for the production of the needed data. The methodology is designed with a focus on economic time series but it could be considered even for other statistical areas. An empirical example is presented: we analyze the back-calculation of Eu15 Industrial Production Index comparing our approach with the Eurostat official one.

Suggested Citation

  • Massimiliano Caporin & Domenico Sartore, 2006. "Methodological aspects of time series back-calculation," Working Papers 2006_56, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2006_56
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    References listed on IDEAS

    as
    1. Andreas Beyer & Jurgen A. Doornik & David F. Hendry, 2000. "Reconstructing Aggregate Euro‐zone Data," Journal of Common Market Studies, Wiley Blackwell, vol. 38(4), pages 613-624, November.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Monica Billio & Laurent Ferrara & Dominique Guegan & Gian Luigi Mazzi, 2009. "Evaluation of Nonlinear time-series models for real-time business cycle analysis of the Euro," Documents de travail du Centre d'Economie de la Sorbonne 09053, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    2. Monica Billio & Massimiliano Caporin & Guido Cazzavillan, 2008. "Dating EU15 monthly business cycle jointly using GDP and IPI," Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2007(3), pages 333-366.
    3. Billio, Monica & Casarin, Roberto & Ravazzolo, Francesco & van Dijk, Herman K., 2012. "Combination schemes for turning point predictions," The Quarterly Review of Economics and Finance, Elsevier, vol. 52(4), pages 402-412.
    4. Terry Clark & Thomas Martin Key, 2021. "The methodologies of the marketing literature: mechanics, uses and craft," AMS Review, Springer;Academy of Marketing Science, vol. 11(3), pages 416-431, December.
    5. Monica Billio & Roberto Casarin, 2010. "Identifying business cycle turning points with sequential Monte Carlo methods: an online and real-time application to the Euro area," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 145-167.
    6. Billio Monica & Casarin Roberto, 2011. "Beta Autoregressive Transition Markov-Switching Models for Business Cycle Analysis," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 15(4), pages 1-32, September.

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    More about this item

    Keywords

    benchmarking; retropolation; historical reconstruction; back-forecasting; missing past values; aggregation; disaggregation.;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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