Economic analysis using higher frequency time series: Challenges for seasonal adjustment

Arbeitspapier

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.

Object Details

• ISBN: 978-3-95729-863-8
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Deutsche Bundesbank Discussion Paper ; No. 53/2021

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes ; Econometric Software ; General Outlook and Conditions
• Subject: COVID-19 ; DSA ; Calendar adjustment ; Time series characteristics

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Ollech, Daniel
• Event: Veröffentlichung
• (who): Deutsche Bundesbank
• (where): Frankfurt a. M.
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/248738
• Last update: 10.03.2025, 11:43 AM CET