A High-Frequency GDP Indicator for Switzerland

Philipp Kronenberg

EconStor Preprints from ZBW - Leibniz Information Centre for Economics

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.

Keywords: Dynamic Factor Model; High-Frequency Data; Business Cycle Index; Economic Activity Indicator; Covid-19 (search for similar items in EconPapers)
JEL-codes: C11 C32 C38 C53 E32 E37 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:esprep:330303

DOI: 10.2139/ssrn.4875922

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