Nowcasting GDP Growth on a Weekly Basis : Leveraging Comprehensive News Article Information and Macroeconomic Indicators

Abstract

Timely economic indicators are crucial for effective macroeconomic decision-making. In particular, this applies during crises when economic shifts can be large and costly. In this thesis, we produce weekly Gross Domestic Product (GDP) growth estimates for 22 large Western economies using a neural network ensemble and a unique combination of macroeconomic data and text variables derived from 686 390 news articles. Our contribution to the literature on GDP growth nowcasting is investigating whether comprehensive information about the news coverage combined with macroeconomic indicators can be leveraged to improve nowcasting. Specifically, we combine sentiment analysis with macroeconomic variables and news topic analysis through zero-shot classification. We find that our model effectively captures the GDP growth trend in most countries during normal times and excels during crises, such as the Global Financial Crisis and the COVID-19 pandemic. Analyzing Shapley values revealed that the ensemble model identifies interaction effects and non-linearities, which enable it to capture sharp shifts during crises.