Microeconometrics
A.Y. 2022/2023
Learning objectives
The course is designed to introduce students to the most common econometric methods in the literature to deal with causal inference. Methods will be analysed in detail, but focus will be on the intuition behind the method. The course will cover regression models, matching, instrumental variable models, along with regression discontinuity, difference in difference and panel data models. Applications will be considered throughout with the use of statistical software Stata.
Expected learning outcomes
At the end of the course students will be able to formulate research questions that deal with causal effects. Students will be able to select and apply the most appropriate method to identify parameters of interest. They will also be able to appreciate pros and cons of each method in different contexts.
Lesson period: First trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
First trimester
Teaching is blended (in-person and online). Links to the online lectures on Teams are available on the Ariel platform.
Course syllabus
1. Economic Questions and the problem of causal inference
2. Simple regression model: properties and assumptions of the Ordinary Least Squares (OLS) estimator
3. Multiple regression model: estimation and inference
4. Limited dependent variable models: basic notions of Logit and Probit models; Linear probability model
5. Instrumental variables estimation and the Two Stage Least Squares (TSLS) estimator
6. Additional topics in Instrumental variables: specification tests and LATE.
7. Introduction to panel data: fixed effects and random effects models
2. Simple regression model: properties and assumptions of the Ordinary Least Squares (OLS) estimator
3. Multiple regression model: estimation and inference
4. Limited dependent variable models: basic notions of Logit and Probit models; Linear probability model
5. Instrumental variables estimation and the Two Stage Least Squares (TSLS) estimator
6. Additional topics in Instrumental variables: specification tests and LATE.
7. Introduction to panel data: fixed effects and random effects models
Prerequisites for admission
Introductory course in Statistics, including notions of inferential statistics. Notions of calculus and matrix algebra.
Teaching methods
Lectures and tutorials
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
SECS-P/01 - ECONOMICS - University credits: 3
SECS-P/05 - ECONOMETRICS - University credits: 3
SECS-P/05 - ECONOMETRICS - University credits: 3
Lessons: 40 hours
Professor:
De Nadai Michele
Educational website(s)
Professor(s)