IDEAS home Printed from
   My bibliography  Save this article

The Modeling and Seasonal Adjustment of Weekly Observations


  • Harvey, Andrew
  • Koopman, Siem Jan
  • Riani, Marco


Several important economic time series are recorded on a particular day every week. Seasonal adjustment of such series is difficult because the number of weeks varies between 52 and 53 and the position of the recording day changes from year to year. In addition certain festivals, most notably Easter, take place at different times according to the year. This article presents a solution to problems of this kind by setting up a structural time series model that allows the seasonal pattern to evolve over time and enables trend extraction and seasonal adjustment to be carried out by means of state-space filtering and smoothing algorithms. The method is illustrated with a Bank of England series on the money supply.

Suggested Citation

  • 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.
  • Handle: RePEc:bes:jnlbes:v:15:y:1997:i:3:p:354-68

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Juan Sebastián Becerra C. & Luis Ceballos S. & Felipe Córdova F. & Michael Pedersen, 2010. "Market Interest Rate Dynamics in Times of Financial Turmoil," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 13(1), pages 5-22, April.
    2. Siem Jan Koopman & Marius Ooms, 2003. "Time Series Modelling of Daily Tax Revenues," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(4), pages 439-469.
    3. Kaushik Bhattacharya & Sunny Kumar Singh, 2016. "Impact of Payment Technology on Seasonality of Currency in Circulation: Evidence from the USA and India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 14(1), pages 117-136, June.
    4. Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2012. "Forecasting weekly Canary tomato exports from annual surface data," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126364, International Association of Agricultural Economists.
    5. Ito, R., 2016. "Spline-DCS for Forecasting Trade Volume in High-Frequency Finance," Cambridge Working Papers in Economics 1606, Faculty of Economics, University of Cambridge.
    6. Marek Hlavacek & Michael Konak & Josef Cada, 2005. "The Application of Structured Feedforward Neural Networks to the Modelling of Daily Series of Currency in Circulation," Working Papers 2005/11, Czech National Bank, Research Department.
    7. repec:ebl:ecbull:eb-17-00140 is not listed on IDEAS
    8. Diego Bodas & Juan Ramon Garcia & Juan Murillo & Matias Pacce & Tomasa Rodrigo & Juan de Dios Romero & Pep Ruiz & Camilo Ulloa & Heribert Valero, 2018. "Measuring Retail Trade Using Card Transactional Data," Working Papers 18/03, BBVA Bank, Economic Research Department.
    9. Caceres-Hernandez, Jose & Martin-Rodriguez, Gloria, 2015. "Splines and seasonal unit roots in weekly agricultural prices," 2015 Conference, August 9-14, 2015, Milan, Italy 211380, International Association of Agricultural Economists.
    10. Alberto Cabrero & Gonzalo Camba-Mendez & Astrid Hirsch & Fernando Nieto, 2009. "Modelling the daily banknotes in circulation in the context of the liquidity management of the European Central Bank," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(3), pages 194-217.
    11. Höhle, Michael & Paul, Michaela, 2008. "Count data regression charts for the monitoring of surveillance time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4357-4368, May.
    12. Diego Elías & Matías Vicens, 2012. "Bills and Coins Daily Demand Forecast," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(65-66), pages 23-39, September.
    13. Serena Ng, 2017. "Opportunities and Challenges: Lessons from Analyzing Terabytes of Scanner Data," NBER Working Papers 23673, National Bureau of Economic Research, Inc.
    14. Rodriguez, Gloria Martin & Hernandez, Jose Juan Caceres, 2005. "Evolving Seasonal Pattern of Tenerife Tomato Exports," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24501, European Association of Agricultural Economists.
    15. Ito, Ryoko, 2013. "Modeling Dynamic Diurnal Patterns in High-Frequency Financial Data," Cambridge Working Papers in Economics 1315, Faculty of Economics, University of Cambridge.
    16. Rodriguez, Gloria Martin & Hernandez, Jose Juan Caceres, 2002. "Canary Island Tomato Exports: A Structural Analysis of Seasonality," 2002 International Congress, August 28-31, 2002, Zaragoza, Spain 24901, European Association of Agricultural Economists.
    17. Bhattacharya, Rudrani & Patnaik, Ila & Shah, Ajay, 2008. "Early warnings of inflation in India," Working Papers 08/54, National Institute of Public Finance and Policy.
    18. Dewenter, Ralf & Heimeshoff, Ulrich, 2016. "Predicting advertising volumes: A structural time series approach," DICE Discussion Papers 228, University of Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    19. Martín Rodríguez, G. & Cáceres Hernández, J. J. & Guirao Pérez, G, 2005. "Un modelo para la exportación semanal de tomate de Almería/A model for the Weekly Tomato Exports from Almería," Estudios de Economía Aplicada, Estudios de Economía Aplicada, vol. 23, pages 731-751, Diciembre.
    20. Mauro Bernardi & Lea Petrella, 2015. "Multiple seasonal cycles forecasting model: the Italian electricity demand," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 24(4), pages 671-695, November.
    21. José Juan Cáceres-Hernández & Gloria Martín-Rodríguez, 2007. "Heterogeneous Seasonal Patterns in Agricultural Data and Evolving Splines," The IUP Journal of Agricultural Economics, IUP Publications, vol. 0(3), pages 48-65, July.
    22. Koopman, Siem Jan & Franses, Philip Hans, 2002. " Constructing Seasonally Adjusted Data with Time-Varying Confidence Intervals," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 64(5), pages 509-526, December.
    23. Martin-Rodriguez, Gloria & Caceres-Hernandez, Jose Juan, 2009. "The Proportion of the Seasonal Period as a Season Index in Weekly Agricultural Data," 2009 Conference, August 16-22, 2009, Beijing, China 49956, International Association of Agricultural Economists.
    24. Jalles, Joao Tovar, 2009. "Structural Time Series Models and the Kalman Filter: a concise review," FEUNL Working Paper Series wp541, Universidade Nova de Lisboa, Faculdade de Economia.

    More about this item


    Access and download statistics


    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:bes:jnlbes:v:15:y:1997:i:3:p:354-68. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum). General contact details of provider: .

    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.

    We have no references for this item. You can help adding them by using 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.