The course introduces students to statistical and econometric techniques aimed at empirically addressing economic and financial case studies. Practices, coupled with the use of statistical software for the analysis of real-world data will allow students to gain their abilities in collecting, analyzing, and interpreting macro and micro data. The course also develops digital competences as of EU DIGCOMP 2.1 (Competence area 1: Information and data literacy; Competence area 2: Communication and collaboration; Competence area 3: Digital content creation).

Theoretical and applied lectures (with real case studies). Linear regression with multiple regressors. Hypothesis tests and confidence intervals in multiple regression. The econometric theory of regression analysis. Nonlinear regression functions. Estimation Causal Effects. Regression models for panel data. Regression models for binary variables. Practice Sessions. Practice sessions are designed to develop the students’ ability in collecting and analyzing data in economics, finance or management, and in analyzing them also by means of statistical software and advanced spreadsheet (Python).

Knowledge and understanding: knowledge of data types and related univariate analysis techniques, including simple linear regression model, multiple linear regression model, regression models for panel data, regression models for binary variables. Applying knowledge and understanding: ability in selecting appropriate data analysis methods, and in analyzing relationships among variables in economics, finance and management Making judgements: ability in collecting, using and critically interpreting quantitative and qualitative data related to economics, finance and management, achieved through the analysis of documents issued by official national and international statistics, scientific articles on statistical methods and applications, case studies. Digital competences are developed. Communication skills: ability to spot and present the most suitable framework for the empirical analysis based on the nature of the economic-financial data at hand and effective communication of data analysis results. Learning skills: ability to learn autonomously data analysis techniques, in professional activities or subsequent studies, achieved through the analysis of econometric methods applied in economics, finance and management.

Stock, J. H. and Watson, M. W. Introduction to Econometrics, (fourth edition), Pearson Ed. Greene, W.H. (2011). Econometric Analysis (seventh edition), Prentice Hall.

Lectures, exercises, case studies in social sciences based on real data, also using statistical and econometric packages Case studies, Data from official statistical sources

Students will be evaluated based on a written exam (75% of the grade), consisting of both theoretical and empirical questions covering the entire program, also including mathematical proofs. The exam may include both open and multiple-choice questions. Students are required to be equipped with a basic pocket calculator. During the exam it is not allowed to consult books or class notes. The midterm will be a written test (25% of the grade) to take place during week 7. The midterm will mirror the structure of the final exam and will include both theoretical and empirical questions covering the course content. Bonus points. Students can earn up to 2 bonus points (on a 30-point scale) by completing an optional empirical project. Bonus points will be added to the final exam grade only if all the following conditions are met: 1. It is the student’s first attempt at the exam. 2. The student achieves a minimum score of 18/30 on the final exam. 3. The exam is taken during the winter exam session.

The degree thesis involves the application of statistical-econometric methodologies in economics or finance. The topic and the criteria for its assignment will be discussed with the instructor.