Nowcasting GDP growth using media news articles

Description

GDP growth is the key indicator for measuring the state of the economy. It is closely monitored by policymakers, firms, and investors to optimize their economic decision-making. But while decisions have to be made in real-time, GDP growth is only observed at a quarterly frequency and with a substantial publication lag. To monitor the state of the economy in real-time, high-frequency macroeconomic and financial variables are used to nowcast GDP growth. We propose to augment this real-time dataset with information embedded in media news articles. The regression-based nowcasting approaches allow us to construct media-based variables that are optimal for the application of nowcasting GDP growth. Moreover, by enforcing sparsity, we manage only to select the most informative macroeconomic, financial, and media-based variables leading to superior nowcasting performance.

Periode	20 dec 2020
Evenementstitel	14th International Conference on Computational and Financial Econometrics
Evenementstype	Conference