Domenico Giannone is an internationally recognized economist who develops economic models grounded in rigorous statistical and economic theory to meet the challenges of monitoring macroeconomic risks in real time, ensuring that policymakers are equipped with the most accurate and timely information. 

Giannone’s research in big data econometrics addresses the fundamental challenge of analyzing vast amounts of economic data to extract meaningful signals about economic conditions. His work focuses on developing methods to handle high-dimensional datasets while ensuring interpretability and robustness. One of Giannone’s most influential contributions has been the development of nowcasting, a real-time economic forecasting method. Giannone has also developed the growth-at-risk framework to predict the full distribution of future outcome, not just the most likely. Instead of predicting the average economic growth, this framework focuses on predicting vulnerabilities as a way to gain understanding of how the economy might perform in the future and assess the risk profile of different policy choices. Recently, Giannone has been developing methods to monitor economic activity and risks globally, with particular attention to low-income countries, which account for a small share of global income but a large share of the global population. Giannone aims to supplement the scarcity of official data by exploring new alternative data sources, most notably, using language as data, which may be possible with recent advancements in artificial intelligence. 

Domenico Giannone joined Johns Hopkins University as a Bloomberg Distinguished Professor in 2025 from the International Monetary Fund.