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Seasonal adjustment of daily time series

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  • Ollech, Daniel

Abstract

Currently, the methods used by producers of official statistics do not facilitate the seasonal and calendar adjustment of daily time series, even though an increasing number of series with daily observations are available. The aim of this paper is the development of a procedure to estimate and adjust for periodically recurring systematic effects and the influence of moving holidays in time series with daily observations. To this end, an iterative STL based seasonal adjustment routine is combined with a RegARIMA model for the estimation of calendar and outlier effects. The procedure is illustrated and validated using the currency in circulation in Germany and a set of simulated time series. A comparison with established methods used for the adjustment of monthly data shows that the procedures estimate similar seasonally adjusted series. Thus, the developed procedure closes a gap by facilitating the seasonal and calendar adjustment of daily time series.

Suggested Citation

  • Ollech, Daniel, 2018. "Seasonal adjustment of daily time series," Discussion Papers 41/2018, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdps:412018
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    File URL: https://www.econstor.eu/bitstream/10419/183487/1/dkp-41.pdf
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    Cited by:

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    2. Daniel Ollech & Deutsche Bundesbank, 2023. "Economic analysis using higher-frequency time series: challenges for seasonal adjustment," Empirical Economics, Springer, vol. 64(3), pages 1375-1398, March.
    3. Sarb, 2021. "Occasional Bulletin of Economic Notes combined 2101," Occasional Bulletin of Economic Notes 11014, South African Reserve Bank.
    4. Natalia Turdyeva & Anna Tsvetkova & Levon Movsesyan & Alexey Porshakov & Dmitriy Chernyadyev, 2021. "Data of Sectoral Financial Flows as a High-Frequency Indicator of Economic Activity," Russian Journal of Money and Finance, Bank of Russia, vol. 80(2), pages 28-49, June.
    5. Katrin Assenmacher & Franz Seitz & Jörn Tenhofen, 2019. "The demand for Swiss banknotes: some new evidence," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 155(1), pages 1-22, December.
    6. Arim Jin & Dahan Lee & Jong-Bae Park & Jae Hyung Roh, 2023. "Day-Ahead Electricity Market Price Forecasting Considering the Components of the Electricity Market Price; Using Demand Decomposition, Fuel Cost, and the Kernel Density Estimation," Energies, MDPI, vol. 16(7), pages 1-19, April.
    7. Ivan Aleksandrovich Kopytin & Alexander Oskarovich Maslennikov & Stanislav Vyacheslavovich Zhukov, 2022. "Europe in World Natural Gas Market: International Transmission of European Price Shocks," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 8-15, May.
    8. Lourenço, Nuno & Rua, António, 2021. "The Daily Economic Indicator: tracking economic activity daily during the lockdown," Economic Modelling, Elsevier, vol. 100(C).
    9. Seyma Gozuyilmaz & O. Erhun Kundakcioglu, 2021. "Mathematical optimization for time series decomposition," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 733-758, September.
    10. Byron Botha & Nqaba Duma & Daan Steenkamp, 2021. "A Truckometer for South Africa," Occasional Bulletin of Economic Notes 11009, South African Reserve Bank.
    11. Byron Botha & Nqaba Duma & Daan Steenkamp, 2021. "Occasional Bulletin of Economic Notes 2101 A Truckometer for South Africa," Occasional Bulletin of Economic Notes 11015, South African Reserve Bank.
    12. Ollech, Daniel, 2021. "Economic analysis using higher frequency time series: Challenges for seasonal adjustment," Discussion Papers 53/2021, Deutsche Bundesbank.
    13. Ángel Cuevas & Ramiro Ledo & Enrique M. Quilis, 2021. "Seasonal adjustment of the Spanish sales daily data," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(4), pages 687-708, December.
    14. Wegmüller, Philipp & Glocker, Christian & Guggia, Valentino, 2023. "Weekly economic activity: Measurement and informational content," International Journal of Forecasting, Elsevier, vol. 39(1), pages 228-243.

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    More about this item

    Keywords

    Seasonal adjustment; STL; Daily time series; Seasonality;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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