Econometrics
A.Y. 2026/2027
Learning objectives
The objective of Econometrics is to provide students with the basic principles of the econometric analysis. All the theoretical aspects of the econometric modelling will be treated jointly with interesting and modern empirical applications in order to motivate students and try to respond to real-world questions with specific numerical answers.
Expected learning outcomes
By the end of the module you will be able to:
Estimate a model using Least Squares.
Interpret the regression estimate and the computer output.
Apply diagnostics to check if the model is correctly specified and propose procedures to correct the miss-specification.
Run an independent analysis in an applied project.
Estimate a model using Least Squares.
Interpret the regression estimate and the computer output.
Apply diagnostics to check if the model is correctly specified and propose procedures to correct the miss-specification.
Run an independent analysis in an applied project.
Lesson period: Second trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second trimester
Course syllabus
1. Introduction to the problems of causal inference
2. Linear regression model: properties and assumptions of the
Ordinary Least Squares (OLS) estimator.
3. Non-linear regression models, including Logit and Probit
models; Linear Probability Model.
4. Endogeneity and Instrumental Variables estimation.
5. Heterogeneous Effects and LATE
6. Regression Discontinuity Design and Difference in Difference
as identification strategies.
7. Introduction to Time Series models
2. Linear regression model: properties and assumptions of the
Ordinary Least Squares (OLS) estimator.
3. Non-linear regression models, including Logit and Probit
models; Linear Probability Model.
4. Endogeneity and Instrumental Variables estimation.
5. Heterogeneous Effects and LATE
6. Regression Discontinuity Design and Difference in Difference
as identification strategies.
7. Introduction to Time Series models
Prerequisites for admission
Introductory course in Statistics, including notions of inferential statistics. Notions of calculus and matrix algebra.
Teaching methods
Frontal lectures and practical examples in class using statistical software.
Teaching Resources
Wooldridge J. "Introductory Econometrics"
Angrist J., Pischke J.S. "Mostly Harmless Econometrics"
Stock J., Watson M. "Introduction to Econometrics"
Angrist J., Pischke J.S. "Mostly Harmless Econometrics"
Stock J., Watson M. "Introduction to Econometrics"
Assessment methods and Criteria
Written exam divided into two sections, worth 14 and 16 points out of 30, respectively. The first section consists of five or six multiple-choice questions, while the second includes several open-ended questions requiring students to interpret a real-world dataset based on the statistical software output provided.
Professor(s)