A High-Frequency GDP Indicator for Switzerland

Abstract

This paper presents a weekly GDP indicator for Switzerland, which addresses the limitations of existing economic activity indicators using alternative high-frequency data created in the wake of the COVID-19 pandemic. The indicator is obtained from a Bayesian mixed-frequency dynamic factor model that integrates conventional macroeconomic and alternative high-frequency data at weekly, monthly, and quarterly frequencies. 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, the model is able to extract business cycle information from a wide range of data frequencies and capture the large and sudden fluctuations during the COVID-19 pandemic. An empirical application illustrates that the indicator accurately approximates weekly quarteron- quarter GDP growth for Switzerland and provides valuable information on the trajectory of GDP at high-frequency, especially during crisis periods. Finally, a pseudo-real-time analysis demonstrates credible nowcasts and a fast convergence towards its final version.