Forecasting and Nowcasting with Text as Data

Mon, 01 Jan 0001 00:00:00 +0000

We will learn how to transform unstructured text into usable signals, evaluate models in applied settings, and connect these tools to questions in the social sciences and policy.

The course covers a range of methods—from simple dictionary-based approaches to supervised models and modern LLM-based techniques—highlighting their strengths, limitations, and trade-offs. A central focus is on building text-based indicators to nowcast real-world events and forecast risks in applied contexts.

Particular emphasis is placed on fine-tuning, model evaluation, and threshold selection, with decisions guided by policy-relevant trade-offs (e.g., false positives vs. missed events). We also discuss why frequency mismatches in data matter, and introduce mixed-frequency methods to better integrate information from different sources.

Teaching on Renato Vassallo

Forecasting and Nowcasting with Text as Data