OVERVIEW

This course provides an introduction to the fundamental concepts of econometrics, equipping students with the tools needed to analyze economic data and estimate economic relationships using statistical models. It combines theoretical lectures with practical applications, with the goal of developing a solid understanding of econometric methodology and its use in empirical research.

AIMS AND CONTENT

LEARNING OUTCOMES

The course aims at providing the students with the basic tools of econometrics analysis and helping them to develop a way of thinking in quantitative terms. Without neglecting a proper discussion of the theoretical background, the course distinctly follows an applied approach. Indeed, by using advanced statistical software, the students will learn how to apply econometric techniques to a number of real-world case studies.

AIMS AND LEARNING OUTCOMES

By the end of the course, students will be able to:

• Understand key econometric techniques used in economic data analysis;

• Formulate and estimate linear and nonlinear regression models;

• Interpret regression output and assess the reliability of estimates;

• Identify main threats to internal validity and model specification issues

PREREQUISITES

Students are expected to have the knowledge typically acquired in undergraduate courses in Mathematics and Statistics.

In particular, familiarity with the following statistical concepts is required: hypothesis testing, t-tests and F-tests, distributions, and density functions.

TEACHING METHODS

• The course is delivered through in-person lectures. Introductory lab sessions on the statistical software Stata are also planned; students are encouraged to bring their own laptops for these sessions.

  Students with disabilities or specific learning disorders (SLD) are kindly asked to contact the instructor via email at the beginning of the course.

SYLLABUS/CONTENT

Course Content

1. Review of Statistics and Probability
   Distributions, random variables, expectations, variance, statistical inference.

2. Simple Linear Regression
   OLS estimation, coefficient interpretation, properties of estimators.

3. Multiple Linear Regression
   Multicollinearity, statistical significance, R², dummy variables, interactions.

4. Internal Validity Issues
   Omitted variable bias, reverse causality, measurement error.

5. Nonlinear Models: Logistic Regression
   Conditional probabilities, interpretation of logit/probit coefficients.

6. Panel Data Analysis
   Fixed and random effects models, unobserved heterogeneity.

7. Time Series Regression
   Trends and seasonality, autocorrelation, stationarity, dynamic models.

RECOMMENDED READING/BIBLIOGRAPHY

• Stock & Watson (2020). Introduction to Econometrics, Fifth Edition, Pearson.

  • Introduction and review of probability and statistics (Chapters 1–3)

  • Regression analysis I (Chapters 4–7, 9)

  • Regression analysis II (Chapters 10–11, 14)

  • Time series (Chapters 15–16)

Additional materials (slides, articles, datasets, and code) will be made available on AulaWeb.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

The lectures begin in the first semester (September).

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam will be written. Students attending the course will also have the option to carry out a group project, which will be presented and discussed at the end of the semester. The project can be worth up to 3 bonus points, which will be added to the written exam grade—provided the exam is taken during the winter session.

ASSESSMENT METHODS

The final grade will be based on a written exam (1.5 hours) consisting of three sections:

• The first section requires students to interpret tables reporting empirical model estimates and perform some calculations (15 points out of 30).

• The second section involves formulating an econometric model based on a described research question (7.5 points).

• The third section consists of theoretical questions (7.5 points out of 30).
  The final grade will be the sum of the points earned across all three sections.

Students attending the course will have the opportunity to work on a group project. Each student will be assigned to a group at the beginning of the semester (details will be published and communicated via AulaWeb). The project may involve preparing Stata code for data analysis and writing a short report, or presenting and discussing an empirical scientific article in class. Students will have time to work on the project—including during class sessions—throughout the semester. Presentations will take place during the final week of lectures, and the report along with the code must be submitted before the first available exam session. The group project may contribute up to 3 bonus points to the final exam grade.
Active participation in classroom exercises may also be rewarded.

FURTHER INFORMATION

Attendance is recommended. Non-attending students are required to prepare for the same syllabus and will take the exam under the same conditions as attending students.