Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/235102 
Year of Publication: 
2020
Series/Report no.: 
KOF Working Papers No. 488
Publisher: 
ETH Zurich, KOF Swiss Economic Institute, Zurich
Abstract: 
Most macroeconomic indicators failed to capture the sharp economic fluctuations during the Corona crisis in a timely manner. Instead, alternative high-frequency data have been used, aiming to monitor the economic situation. However, these data are often only loosely related to the business cycle and come with irregular patterns of missing observations, ragged edges and short histories. This paper presents a novel mixedfrequency dynamic factor model for measuring economic activity at high-frequency intervals in rich data environments. Previous research has estimated the dynamic factor conditional on actually observed data only. In contrast, we propose to estimate the dynamic factor conditional on a balanced panel with observed and latent data information, where the latent data are themselves estimated in a separate state-space block. One benefit of this data augmentation strategy is that it allows to easily account for serial correlation in the factor measurement errors. We apply the model to a set of daily, weekly, monthly and quarterly series and extract a dynamic factor, which is identified as the weekly growth rate of GDP. It turns out that the model is well suited to exploit the business cycle information contained in alternative highfrequency data. GDP is tracked timely and accurately during the Corona crisis and past economic crises.
Subjects: 
Economic Activity Indicator
Real Time
Nowcasting
Alternative High-Frequency Data
Mixed-Frequency Dynamic Factor Model
Data Augmentation
JEL: 
C11
C32
C38
C53
E32
E37
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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