Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/308864 
Year of Publication: 
2022
Citation: 
[Journal:] Empirical Economics [ISSN:] 1435-8921 [Volume:] 64 [Issue:] 3 [Publisher:] Springer [Place:] Berlin, Heidelberg [Year:] 2022 [Pages:] 1375-1398
Publisher: 
Springer, Berlin, Heidelberg
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.
Subjects: 
COVID-19
DSA
Calendar adjustment
Time series characteristics
JEL: 
C14
C22
C87
E66
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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