Seasonal Adjustment of Daily Data with CAMPLET

Barend Abeln () and Jan Jacobs

Chapter Chapter 6 in Seasonal Adjustment Without Revisions, 2023, pp 63-78 from Springer

Abstract: 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.

Date: 2023
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Working Paper: Seasonal adjustment of daily data with CAMPLET (2022)
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DOI: 10.1007/978-3-031-22845-2_6

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