Course 2025-2026 a.y.

The following outline is preliminary and will be adapted based on the needs of the course:

1. What is econometrics and why is it useful?
   • Economic questions, data analysis, and causality
2. Linear regression
   • Linear predictors
   • Inference
   • The classical regression model (including multivariate regression)
   • Internal and external validity
3. Panel data
4. Further topics in regression analysis
   • Nonlinear regression functions
   • Binary choice models
5. Instrumental variables

6. Introduction to program evaluation: experiments and quasi-experiments

7. (Time permitting) Prediction with many regressors and big data

8. Introduction to time series and forecasting

We will review probability and statistics as needed throughout the course. Relevant topics include:

• Random variables, probability distributions, and densities. Expectations. Multivariate

distributions, conditional distributions, and independence. Special distributions: Bernoulli, Normal, Chi-squared, F, and t.

• Large sample theory.
• Estimators and their properties. Confidence intervals and hypothesis testing.

• Define and explain the fundamental concepts and goals of econometrics.

• Identify appropriate econometric methods for analyzing economic relationships.

• Recognize and assess the assumptions underlying linear regression and related models.

• Apply econometric reasoning to evaluate the validity and reliability of empirical analyses.

• Interpret and critically discuss empirical results in a given economic problem.

• Apply econometric methods to analyze economic relationships.

• Use econometric software (R) to perform empirical analyses.

• Evaluate the validity and interpretation of estimation and testing procedures.

• Assess the statistical and economic significance of empirical results.

• Communicate empirical findings effectively.

• Lectures
• Practical Exercises
• Individual works / Assignments
• Collaborative Works / Assignments

The course includes lectures and problem sets, part of which will be conducted using R, a free, open-source software environment for statistical computing and data analysis. Familiarity with R is not expected.

Students will be evaluated on the basis of the following components:

• Written Examinations (90% of the final grade). Students can choose between two assessment options:

  1. Taking the first and second partial exams, weighted 40% and 60%, respectively.

  2. A single comprehensive final exam, accounting for 100% of the grade.

• Class Assignment (10% of the final grade). Students will prepare a written report on an empirical paper or econometric problem, demonstrating their understanding and critical reflection on the course material. Evaluation may include group presentations during class.

Problem sets will be assigned throughout the term for practice but will not be graded. Students “fuoricorso” (those who have not completed their study plan within the standard period) must take the ongoing academic year's written exam.

Lecture slides will be posted on the course website. Slides are designed to be self-contained when complemented by regular class attendance.

Recommended textbooks (optional, earlier editions are also acceptable):

• James H. Stock & Mark W. Watson (2019). Introduction to Econometrics (4th ed.). Pearson.

• Jeffrey M. Wooldridge (2025). Introductory Econometrics: A Modern Approach (8th ed.). Cengage Learning.

• Massimiliano Marcellino (2016). Applied Econometrics: An Introduction. Bocconi University Press.

Students are strongly encouraged to read along with Stock & Watson, as a substantial portion of the course material is based on this textbook.