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Economic analysis using higher-frequency time series: challenges for seasonal adjustment

Author

Listed:
  • Daniel Ollech

    (Central Office, Directorate General Statistics)

  • Deutsche Bundesbank

    (Central Office, Directorate General Statistics)

Abstract

The COVID-19 pandemic has increased the need for timely and granular information to assess the state of the economy in real time. Weekly and daily indices have been constructed using higher-frequency data to address this need. Yet the seasonal and calendar adjustment of the underlying time series is challenging. Here, we analyse the features and idiosyncracies of such time series relevant in the context of seasonal adjustment. Drawing on a set of time series for Germany—namely hourly electricity consumption, the daily truck toll mileage, and weekly Google Trends data—used in many countries to assess economic development during the pandemic, we discuss obstacles, difficulties, and adjustment options. Furthermore, we develop a taxonomy of the central features of seasonal higher-frequency time series.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:empeco:v:64:y:2023:i:3:d:10.1007_s00181-022-02287-5
    DOI: 10.1007/s00181-022-02287-5
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    References listed on IDEAS

    as
    1. Vipin Arora & Shuping Shi, 2016. "Energy consumption and economic growth in the United States," Applied Economics, Taylor & Francis Journals, vol. 48(39), pages 3763-3773, August.
    2. Nicolas Woloszko, 2020. "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers 1634, OECD Publishing.
    3. Gerhard Fenz & Helmut Stix, 2021. "Monitoring the economy in real time with the weekly OeNB GDP indicator: background, experience and outlook," Monetary Policy & the Economy, Oesterreichische Nationalbank (Austrian Central Bank), issue Q4/20-Q1/, pages 17-40.
    4. Ollech, Daniel, 2018. "Seasonal adjustment of daily time series," Discussion Papers 41/2018, Deutsche Bundesbank.
    5. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2022. "Measuring real activity using a weekly economic index," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(4), pages 667-687, June.
    6. Daniel J. Lewis & Karel Mertens & James H. Stock & Mihir Trivedi, 2021. "High-Frequency Data and a Weekly Economic Index during the Pandemic," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 326-330, May.
    7. Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
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    Citations

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    Cited by:

    1. Eraslan, Sercan & Reif, Magnus, 2023. "A latent weekly GDP indicator for Germany," Technical Papers 08/2023, Deutsche Bundesbank.
    2. Robert Lehmann & Sascha Möhrle, 2024. "Forecasting regional industrial production with novel high‐frequency electricity consumption data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(6), pages 1918-1935, September.
    3. Webel, Karsten & Smyk, Anna, 2023. "Towards seasonal adjustment of infra-monthly time series with JDemetra+," Discussion Papers 24/2023, Deutsche Bundesbank.
    4. Webel, Karsten, 2025. "Revisions in concurrent seasonal adjustments of daily and weekly economic time series," Discussion Papers 08/2025, Deutsche Bundesbank.
    5. A. Smyk & K. Webel, 2024. "Vers une désaisonnalisation des séries temporelles infra-mensuelles avec JDemetra+," Documents de Travail de l'Insee - INSEE Working Papers m2024-04, Institut National de la Statistique et des Etudes Economiques.
    6. Woloszko, Nicolas, 2024. "Nowcasting with panels and alternative data: The OECD weekly tracker," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1302-1335.

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    More about this item

    Keywords

    COVID-19; DSA; Calendar adjustment; Time series characteristics;
    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
    • C87 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Econometric Software
    • E66 - Macroeconomics and Monetary Economics - - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook - - - General Outlook and Conditions

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