Applied Macroeconomics and Text Analysis

Catalog description

Economists increasingly use advanced text analysis (NLP) and machine-learning methods to study high-dimensional, unstructured data. This course provides theoretical grounding and hands-on experience applying NLP to core macroeconomic topics, from policymaker communication to patent innovation and media-based sentiment indices. Students will learn methods—dictionary approaches, topic modeling, sentiment analysis, large language models—and see how to integrate textual indicators into macro tools like local projections and factor models. The Python-based assignments will build technical foundations, and the major assessments of the course – a replication study and original research project – will foster skill development toward frontier research.