Title:                     Causal inference and Panel Data econometrics

Instructor:          Elena Lagomarsino, elena.lagomarsino@unige.it

Credit points (CFU):      1

Lectures: 12 hours

Homework: 14 hours

Lectures Period: April-May

Course Description and objectives

This course is divided into two parts. The first part introduces impact evaluation analysis, starting with an explanation of counterfactual analysis. It will then cover randomized controlled trials (RCTs), considered the gold standard in causal inference. Laboratory sessions using Stata will demonstrate the practical application of these methods, along with discussions of prominent scientific studies. Additional impact evaluation methods, such as matching, difference-in-differences, and instrumental variables, will also be briefly introduced.

The second part focuses on panel data analysis, emphasizing key estimation methods for longitudinal data. Similar to the first part, both theoretical and practical aspects will be covered, with empirical applications demonstrated in Stata lab sessions.

Prerequisites

Basic knowledge of Econometrics, Statistics, and Stata.

Course Materials

Lecture slides and code examples, selected book chapters

Recommended textbooks:

Wooldridge, Jeffrey M. Introductory Econometrics: A Modern Approach. 7th ed., Cengage Learning, 2020.

Greene, William H. Econometric Analysis. 8th ed., Pearson, 2018.

Cunningham, Scott Causal Inference: The Mixtape. Yale University Press, 2021.

Angrist, Joshua D., and Jörn-Steffen Pischke. Mastering 'Metrics: The Path from Cause to Effect. Princeton University Press, 2014.

Assessment

Students will be evaluated through a final written  exam