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Multivariate approaches for aggregate time series1

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Abstract

The problem of choosing between the direct and the indirect method for the seasonal adjustment of aggregate time series is addressed. Different multivariate approaches are proposed in order to define properly optimised aggregation weights for the component time series. The new weighing systems help in discriminating between the classical methods and may perform better than the weights assigned a-priori. Applications on real economic series are shown.

Acknowledgments: This research was supported by an ESPRIT (29.741 TESS) grant. We are very much indebted to Prof. Carlo Lauro for providing us with the main ideas behind this paper as well as for strongly supporting and encouraging the further developments.

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Author information

Authors and Affiliations

  1. Dipartimento di Matematica e Statistica, Università degli Studi di Napoli, Federico II - Via Cintia, 80126, Napoli, Italy

    Cristina Davino & Vincenzo Esposito

Authors
  1. Cristina Davino
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  2. Vincenzo Esposito
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Editor information

Editors and Affiliations

  1. Department of Statistical Methods, Statistics Netherlands, P.O. Box 4000, 2270 JM, Voorburg, The Netherlands

    Jelke G. Bethlehem

  2. Faculty of Social Sciences, Department of Methodology and Statistics, Utrecht University, P.O. Box 80140, 3508 TC, Utrecht, The Netherlands

    Peter G. M. van der Heijden

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© 2000 Springer-Verlag Berlin Heidelberg

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Davino, C., Esposito, V. (2000). Multivariate approaches for aggregate time series1 . In: Bethlehem, J.G., van der Heijden, P.G.M. (eds) COMPSTAT. Physica, Heidelberg. https://doi.org/10.1007/978-3-642-57678-2_31

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  • DOI: https://doi.org/10.1007/978-3-642-57678-2_31

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-1326-5

  • Online ISBN: 978-3-642-57678-2

  • eBook Packages: Springer Book Archive

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