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Vers une désaisonnalisation des séries temporelles infra-mensuelles avec JDemetra+

Author

Listed:
  • A. SMYK

    (Insee)

  • K. WEBEL

    (Deutsche Bundesbank)

Abstract

Les séries économiques infra-mensuelles sont devenues de plus en plus populaires dans les statistiques officielles ces dernières années. Cette évolution a été largement favorisée par la transformation numérique de la dernière décennie. La pandémie de COVID-19 en 2020 a renforcé ce phénomène, car de nombreux utilisateurs de données ont immédiatement demandé des données hebdomadaires, voire quotidiennes, sur l'évolution de l'économie. Ces données infra-mensuelles présentent souvent un comportement saisonnier qui nécessite un ajustement. C'est pourquoi JDemetra+, le logiciel officiel de désaisonnalisation des données mensuelles et trimestrielles du Système statistique européen et du Système européen de banques centrales, a été récemment enrichi d'un modèle de pré-ajustement de type regArima et de versions étendues des algorithmes de décomposition basées sur les modèles Arima, STL et X-11, adaptées aux spécificités des données infra-mensuelles. Celles-ci sont accessibles par le biais d'un écosystème de packages R qui permet également d'accéder à une modélisation structurelle des séries temporelles, dans un cadre espace-état. Nous donnons un aperçu complet de ces packages et en illustrons les principales caractéristiques. Nous fournissons des extraits de code R utilisés pour désaisonnaliser les naissances quotidiennes en France, la consommation horaire d'électricité en Allemagne et les demandes initiales hebdomadaires d'assurance chômage aux États-Unis.

Suggested Citation

  • A. Smyk & K. Webel, 2024. "Vers une désaisonnalisation des séries temporelles infra-mensuelles avec JDemetra+," Documents de Travail de l'Insee - INSEE Working Papers m2024-04, Institut National de la Statistique et des Etudes Economiques.
    • Handle: RePEc:nse:doctra:m2024-04
    as

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    References listed on IDEAS

    as
    1. Harvey, Andrew, 2001. "Testing in Unobserved Components Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(1), pages 1-19, January.
    2. [Reference to Proietti], Tommaso, 2000. "Comparing seasonal components for structural time series models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 247-260.
    3. Harvey, Andrew & Koopman, Siem Jan & Riani, Marco, 1997. "The Modeling and Seasonal Adjustment of Weekly Observations," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(3), pages 354-368, July.
    4. Ramanathan, Ramu & Engle, Robert & Granger, Clive W. J. & Vahid-Araghi, Farshid & Brace, Casey, 1997. "Shorte-run forecasts of electricity loads and peaks," International Journal of Forecasting, Elsevier, vol. 13(2), pages 161-174, June.
    5. Marc K. Francke & Siem Jan Koopman & Aart F. De Vos, 2010. "Likelihood functions for state space models with diffuse initial conditions," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(6), pages 407-414, November.
    6. Canova, Fabio & Hansen, Bruce E, 1995. "Are Seasonal Patterns Constant over Time? A Test for Seasonal Stability," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 237-252, July.
    7. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    8. Daniel Ollech & Deutsche Bundesbank, 2023. "Economic analysis using higher-frequency time series: challenges for seasonal adjustment," Empirical Economics, Springer, vol. 64(3), pages 1375-1398, March.
    9. Lilian Shiao‐Yen Wu & J. R. M. Hosking & Nalini Ravishanker, 1993. "Reallocation Outliers in Time Series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(2), pages 301-313, June.
    10. Cancelo, José Ramón & Espasa, Antoni & Grafe, Rosmarie, 2008. "Forecasting the electricity load from one day to one week ahead for the Spanish system operator," International Journal of Forecasting, Elsevier, vol. 24(4), pages 588-602.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    modele Airline etendu; donnees haute-frequence; statistique publique; extraction de signaux; decomposition en composantes inobservables;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General

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