IDEAS home Printed from https://ideas.repec.org/p/bfr/banfra/147.html
   My bibliography  Save this paper

La désaisonnalisation des séries d’agrégats monétaires et de crédit à la Banque de France : aspects théoriques et mise en oeuvre

Author

Listed:
  • Fonteny, E.

Abstract

Since July 2003, the Banque de France has been using seasonally adjusted (SA) data for the monthly reporting of national monetary developments, with renewed statistical tools. Before the start of the single currency in 1999, the Banque de France already calculated seasonally adjusted data, using a rather old method, X11-ARIMA. Due to the shortcomings of this means, the Banque de France has developed a new method of seasonal adjustment, using both TRAMO-SEATS and X12-ARIMA, and defining a specific revision policy for each SA series. his paper aims at presenting and explaining the choices made by the Banque de France regarding the implementation of the new production process of seasonally monetary and loans series. In the meantime, the theoretical background related to the concept of seasonality and to various seasonal adjustment methods is shown in order to throw light on these choices, thus not from a research angle. The paper firstly provides information about the concept of seasonality as well as the existing methods of seasonal adjustment. The new production process of SA monetary data at the Banque de France is then described. Two examples related to the seasonal adjustment of loans to enterprises and to housing loans are included in order to stress the difficulties implied by the monthly production of SA data, as well as the impact on then output of the choices made by the producer regarding the modelling of the seasonality.

Suggested Citation

  • Fonteny, E., 2006. "La désaisonnalisation des séries d’agrégats monétaires et de crédit à la Banque de France : aspects théoriques et mise en oeuvre," Working papers 147, Banque de France.
    • Handle: RePEc:bfr:banfra:147
    as

    Download full text from publisher

    File URL: https://publications.banque-france.fr/sites/default/files/medias/documents/working-paper_147_2006.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Agustín Maravall, 1996. "Unobserved Components in Economic Time Series," Working Papers 9609, Banco de España.
    2. Gabriele Fiorentini & Agustín Maravall, 1995. "Unobserved Components in ARCH Models: An Application to Seasonal Adjustment," Working Papers wp1995_9509, CEMFI.
    3. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Economic Time Series," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 291-320, October.
    4. Bell, William R & Hillmer, Steven C, 1984. "Issues Involved with the Seasonal Adjustment of Time Series: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 343-349, October.
    5. Victor Gómez & Agustín Maravall, 1996. "Programs TRAMO and SEATS, Instruction for User (Beta Version: september 1996)," Working Papers 9628, Banco de España.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Maravall, Agustin, 2006. "An application of the TRAMO-SEATS automatic procedure; direct versus indirect adjustment," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2167-2190, May.
    2. Fok, D. & Franses, Ph.H.B.F. & Paap, R., 2005. "Performance of Seasonal Adjustment Procedures: Simulation and Empirical Results," Econometric Institute Research Papers EI 2005-30, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    3. del Barrio Castro, Tomas & Pons Fanals, Ernest & Surinach Caralt, Jordi, 2002. "The effects of working with seasonally adjusted data when testing for unit root," Economics Letters, Elsevier, vol. 75(2), pages 249-256, April.
    4. Kaiser, Regina & Maravall, Agustin, 2005. "Combining filter design with model-based filtering (with an application to business-cycle estimation)," International Journal of Forecasting, Elsevier, vol. 21(4), pages 691-710.
    5. Kaiser Remiro, Regina & Maravall, Agustín, 1999. "Short-term and long-term trends, seasonal and the business cycle," DES - Working Papers. Statistics and Econometrics. WS 6291, Universidad Carlos III de Madrid. Departamento de Estadística.
    6. Agustín Maravall & Fernando J. Sánchez, 2000. "An Application of TRAMO-SEATS: Model Selection and Out-of-Sample Performance: the Swiss CPI Series," Working Papers 0014, Banco de España.
    7. Maravall, Agustin & Planas, Christophe, 1999. "Estimation error and the specification of unobserved component models," Journal of Econometrics, Elsevier, vol. 92(2), pages 325-353, October.
    8. Gianluca Caporello & Agustín Maravall & Fernando J. Sánchez, 2001. "Program TSW Reference Manual," Working Papers 0112, Banco de España.
    9. Daniel Dzikowski & Carsten Jentsch, 2024. "Structural Periodic Vector Autoregressions," Papers 2401.14545, arXiv.org.
    10. Thornton, Michael A., 2013. "Removing seasonality under a changing regime: Filtering new car sales," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 4-14.
    11. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    12. Proietti, Tommaso & Riani, Marco, 2007. "Transformations and Seasonal Adjustment: Analytic Solutions and Case Studies," MPRA Paper 7862, University Library of Munich, Germany.
    13. Kaiser Remiro, Regina & Maravall, Agustín, 1999. "Seasonal outliers in time series," DES - Working Papers. Statistics and Econometrics. WS 6333, Universidad Carlos III de Madrid. Departamento de Estadística.
    14. Buono, Dario & Alpay, Kocak, 2010. "Backward recalculation of seasonal series affected by economic crisis: a Model-Based-Link method for the case of Turkish GDP," MPRA Paper 40092, University Library of Munich, Germany.
    15. Hayat, Aziz & Bhatti, M. Ishaq, 2013. "Masking of volatility by seasonal adjustment methods," Economic Modelling, Elsevier, vol. 33(C), pages 676-688.
    16. Bollerslev, Tim & Ghysels, Eric, 1996. "Periodic Autoregressive Conditional Heteroscedasticity," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(2), pages 139-151, April.
    17. Ghysels, Eric, 1994. "On the Periodic Structure of the Business Cycle," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 289-298, July.
    18. Jang Hyung Cho & Robert T. Daigler, 2012. "An unbiased autoregressive conditional intraday seasonal variance filtering process," Quantitative Finance, Taylor & Francis Journals, vol. 12(2), pages 231-247, October.
    19. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2009. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 683-713, October.
    20. Barnett, William A. & de Peretti, Philippe, 2009. "Admissible Clustering Of Aggregator Components: A Necessary And Sufficient Stochastic Seminonparametric Test For Weak Separability," Macroeconomic Dynamics, Cambridge University Press, vol. 13(S2), pages 317-334, September.

    More about this item

    Keywords

    Seasonal adjustment methods ; Monetary aggregates ; Outliers ; SARIMA models.;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bfr:banfra:147. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Michael brassart (email available below). General contact details of provider: https://edirc.repec.org/data/bdfgvfr.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.