OVERVIEW

This course introduces students to the fundamental concepts of traditional econometrics, describing the main methodologies for analyzing economic data using statistical models. It is
part of the quantitative teaching units in the curriculum and provides a foundation for advanced economic studies.

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, the student will be able to:
- Apply key econometric techniques to the analysis of real economic data using appropriate statistical software, and assess the correctness of the adopted procedures.
- Formulate and estimate linear and nonlinear regression models on provided datasets, justifying methodological choices.
- Interpret econometric estimation results in relation to specific empirical problems, discussing the significance and reliability of the estimates.
- Identify and discuss main threats to internal validity and model specification in applied contexts, proposing possible solutions.

[Dublin descriptors: knowledge and understanding; applying knowledge and understanding;
making judgements; communication skills; learning skills]

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.
Attendance is not compulsory but strongly recommended.

Students with disabilities or specific learning disorders (SLD) are kindly asked to contact the instructor via email at the beginning of the course. For information on support services and
alternative learning arrangements, please refer to the UniGe website.

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. Supplementary materials will be provided mainly in Italian, unless otherwise specified.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Lectures will begin in September, according to the official schedule published on the DIEC Department website: https://economia.unige.it/didattica/calendari

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. 

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

Erasmus students wll take the same exam. They can request an English version of the exam and take the exam in English

ASSESSMENT METHODS

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

- Interpretation of tables reporting empirical model estimates and calculations (15 points out of 30), to assess the ability to interpret econometric results and apply techniques to real data.
- Formulation of an econometric model based on a described research question (7.5 points), to evaluate the ability to formulate and estimate models.
- Theoretical questions (7.5 points out of 30), to verify knowledge of fundamental concepts. 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. 

Each part of the exam is designed to assess specific declared learning outcomes. Evaluation criteria include: accuracy of answers, clarity of exposition, appropriate use of terminology, and
critical analysis skills.

FURTHER INFORMATION

For further information not included in this teaching unit description, please contact the instructor.