The modeling and seasonal adjustment of weekly observations

Andrew Harvey, Siem Jan Koopman, Marco Riani

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)354-368
Number of pages15
JournalJournal of Business and Economic Statistics
Issue number3
Publication statusPublished - 1 Jan 1997
Externally publishedYes


  • Calendar effects
  • Irregularly spaced observations
  • Kalman filter
  • Money supply
  • Moving festival
  • Periodic spline
  • Stochastic seasonality
  • Structural time series model


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