Instructor: Stephane Bonhomme (University of Chicago)
Dates: 17-21 August 2026
Hours: 9:30 to 13:00 CEST
Format: In person
Intended for
Applied researchers and econometricians interested in estimating economic models using panel data.
Prerequisites
Master’s-level courses in probability and statistics, and econometrics.
Overview
This course provides applied researchers and econometricians with tools to estimate dynamic models using panel data. Particular emphasis is placed on relaxing strict exogeneity assumptions, which are widely used in empirical work-including difference-in-differences designs and event studies-but are often economically and empirically restrictive.
We will review classic methods for estimating linear dynamic panel data models with sequentially exogenous covariates. We will then discuss more recent approaches that extend these methods to settings with nonlinearities, coefficient heterogeneity, and network dynamics.
Topics
- Conceptual foundations: strict exogeneity, sequential exogeneity, and feedback
- Bias in models with dynamic feedback
- Implications for difference-in-differences and event-study designs
- Classic dynamic panel data methods I: GMM
- Classic dynamic panel data methods II: quasi-likelihood and large-T approaches
- Coefficient heterogeneity in models with dynamic feedback
- Nonlinear models with dynamic feedback
- Dynamic models in networks
