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Seasonal Adjustment of Daily Data with CAMPLET

In: Seasonal Adjustment Without Revisions

Author

Listed:
  • Barend Abeln

  • Jan P. A. M. Jacobs

    (University of Groningen)

Abstract

In the last decade, large data sets have become available, both in terms of the number of time series and with higher frequencies (weekly, daily, and even higher). All series may suffer from seasonality, which hides other important fluctuations. Therefore, time series are typically seasonally adjusted. However, standard seasonal adjustment methods cannot handle series with higher than monthly frequencies. Recently, Abeln et al. (2019) presented CAMPLET, a new seasonal adjustment method, which does not produce revisions when new observations become available. The aim of this chapter is to show the attractiveness of CAMPLET for seasonal adjustment of daily time series. We apply CAMPLET to daily data on the gas system in the Netherlands.

Suggested Citation

  • Barend Abeln & Jan P. A. M. Jacobs, 2023. "Seasonal Adjustment of Daily Data with CAMPLET," SpringerBriefs in Economics, in: Seasonal Adjustment Without Revisions, chapter 0, pages 63-78, Springer.
    • Handle: RePEc:spr:spbchp:978-3-031-22845-2_6
    DOI: 10.1007/978-3-031-22845-2_6
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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