Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/330303 
Year of Publication: 
2024
Publisher: 
SSRN, Rochester, NY
Abstract: 
This paper presents a weekly GDP indicator for Switzerland, which addresses the limitations of existing economic activity indicators by using alternative highfrequency data created in response to the COVID-19 pandemic. The indicator is derived from a Bayesian mixed-frequency dynamic factor model, which integrates both conventional macroeconomic and alternative high-frequency data at weekly, monthly, and quarterly frequencies. The model extracts business cycle information from a wide range of data frequencies and captures the large and sudden fluctuations during the pandemic by estimating missing observations as latent states through data augmentation, incorporating stochastic volatility in the state equation, and accounting for serial correlation in the measurement errors. An empirical application shows that the indicator accurately approximates weekly GDP growth for Switzerland and provides valuable information on the trajectory of GDP at high frequency, particularly during crisis periods. A pseudo real-time analysis demonstrates high forecast accuracy at short leads and improvements over other GDP indicators for Switzerland.
Subjects: 
Dynamic Factor Model
High-Frequency Data
Business Cycle Index
Economic Activity Indicator
Covid-19
JEL: 
C11
C32
C38
C53
E32
E37
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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