Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 24, 2022 erstellt
Beschreibung:
Most macroeconomic series failed to capture the sharp fluctuations during the COVID-19 pandemic. Also, it proved difficult to extract business cycle information from alternative high-frequency data. We present a Bayesian mixed-frequency dynamic factor model with stochastic volatility for measuring GDP growth at high-frequency intervals. Its novelty is an additional state-space block, in which the sparse observations in the mixed-frequency data are augmented to a balanced panel with observed and estimated latent information. The dynamic factor is then estimated conditional on the augmented data. Our model exploits the information in rich datasets well, tracking GDP timely and accurately during volatile periods