Course information

Instructor: Sophia Kazinnik (Stanford University)
Dates:  17-21 August 2026
Hours: 15:00 to 18:30 CEST 
Format: In person

Intended for

Researchers, central bank economists, and PhD students who want to use large language models (LLMs) in data-driven research. Researchers working at the intersection of monetary and financial economics who want to go beyond basic text analysis and use LLMs for things like better measurement, generating new research ideas or data, and running agent-based simulations.

Prerequisites

A basic familiarity with coding, econometrics, and statistics. The lectures will use Python notebooks prepared in advance. No prior deep learning experience is required. The course is designed for applied researchers who want to build reproducible workflows with AI that work in real-world settings (including working with confidential data) and who are interested in using LLMs for measurement, generation, and agent-based simulation, particularly in the context of monetary and financial economics.

Overview

Monetary and financial economics increasingly hinge on language and communication, whether we are trying to decode central bank signals, measure narratives in markets, or understand how households and firms form expectations. This course focuses on three complementary ways modern AI can be used in research:

• Extracting economic signals from text (and, when relevant, audio/video): turning complex documents and texts into interpretable measures that can enter standard empirical designs (e.g., stance, uncertainty, risk, guidance, narrative shifts, etc.).
• Producing structured research outputs with generative models: using LLMs to create objects economists actually use: forecast variables, scenario consistent summaries, structured extractions, synthetic survey responses, counterfactual message variants, and scalable measurement targets.
• Simulating economic actors and institutions: building agent-based systems (e.g., forecasters, committee members, depositors, etc.) that interact and deliberate under controlled information sets, creating an “in silico” laboratory for counterfactual policy and market experiments.

A core theme throughout is measurement discipline: we treat LLMs as powerful but fallible instruments and emphasize validation, data leakage safeguards, and error correction, especially when models are used to generate labels or behavioral outcomes at scale.

There are three parts to the course:

• Text as Data (Measurement as Data Creation). Practical approaches for extracting economic signals from text: stance, risk, uncertainty, narratives, and regime shifts, paired with transparent evaluation and error analysis.
• Generation as a Research Tool. Using LLMs to generate research-relevant outputs (e.g., forecast objects, structured extractions, synthetic survey responses, and scalable measurement targets) while being able to validate and “explain” these outputs and enforce constraints when needed.
• Agents and Simulations. Building agent-based workflows that combine personas, real-time/vintage data, and interaction protocols to study phenomena such as expectations formation and decision-making, linking micro-level reasoning and communication to macro/market outcomes.

Topics