- Docente: Denni Tommasi
- Credits: 6
- SSD: SECS-P/05
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Bologna
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Corso:
Second cycle degree programme (LM) in
Statistical Sciences (cod. 9222)
Also valid for First cycle degree programme (L) in Statistical Sciences (cod. 8873)
Learning outcomes
By the end of the course the student should have acquired the basics of econometric modelling. In particular the student should be able: - to specify and estimate linear, single-equation econometric models and to face the endogenous regressors issue; - to perform a specification analysis of the model
Course contents
- Potential outcomes framework
- Matching
- Instrumental variables
- Regression discontinuity design (RDD)
- Difference-in-Differences (DID)
Readings/Bibliography
Scott Cunningham. Causal Inference: The Mixtape. New Haven: Yale University Press, 2021.
Joshua D. Angrist & Jörn‑Steffen Pischke: Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton: Princeton University Press, 2009.
Teaching methods
For each topic we will first introduce the relevant theory, and then move to its empirical application. Special emphasis will be placed on the economic interpretation of the results.
Assessment methods
Written exam, problem sets and in-class participation.
Teaching tools
We will discuss several empirical analysis using the econometric software STATA.
Office hours
See the website of Denni Tommasi