Location: Freeman Commons, Humphrey School 205
Wisdom Takumah, Assistant Professor of Economics, North Carolina A&T State University, explores how AI, machine learning, and big data are reshaping empirical macroeconomics, from recession prediction to nowcasting. Open to the public.
This talk explores how artificial intelligence, machine learning, and big data are transforming empirical macroeconomics and modern econometric practice. As economists gain access to increasingly large, high-frequency, and unstructured datasets, traditional econometric methods are being complemented by tools such as random forests, gradient boosting, neural networks, large-scale factor models, and other AI-based techniques. The discussion will highlight applications in macroeconomic forecasting, recession prediction, nowcasting, and extracting economic signals from high-dimensional datasets. Particular attention will be given to the strengths and limitations of machine learning relative to traditional econometric approaches, especially regarding interpretability and causal inference. The talk will also consider how generative AI and large language models may expand the scope of empirical economic research.
Speaker Biography
Wisdom Takumah is an Assistant Professor in Economics at North Carolina A&T State University. He earned his PhD in Economics from Emory University and a double master’s degree in Data Science and Economics from South Dakota State University. He previously worked as a Research Assistant at the United Nations University World Institute for Development and Economic Research (UNU-WIDER) at the University of Ghana. His research interests include empirical macroeconomics, fiscal and monetary policy, time series forecasting, and machine learning applications to macroeconomics.