The purpose of the course is to provide the necessary tools for a thorough understanding of the theory of econometrics. The course goals are 1) to be able to perform estimation and testing in linear regression models, and to be comfortable with asymptotic theory for linear models, 2) to be able to implement econometric methods as needed for an empirical analyses in economics and finance.

Knowledge and understanding: The course will focus on the main econometric tools to perform quantitative analyses of economic data. This course provides analytical resources that will enable students to autonomously investigate the empirical validity of economic theory and to predict economic data. Applying knowledge and understanding: The students will be able to: • Carry out univariate and multivariate linear regression analyses • Determine the possible presence of causal relationships between economic variables • Adopt the most common estimation methods: linear projections, maximum likelihood, method of moments. Making judgements: We expect students to be able to set up and estimate econometric models and to interpret the outcomes in a sensible way. Students are asked to use the software MATLAB to gain insights on the econometric methodologies and their implementation. Learning skills: This course will contribute to empower learners giving them the theoretical, econometric and programming tools to autonomously study economic and financial data and test economic theories.

Some of the topics covered are: statistic foundations, single-equation linear model, and assumptions of the OLS estimator. Asymptotic properties of OLS and ML estimators. Testing (Likelihood-ratio, Wald, Lagrange Multipliers). Heteroskedasticity. Generalized least square estimator. Systems of equations. Measurement Error. Simultaneous equations model. Omitted variables. Instrumental variables estimation. Models for Panel Data. Generalized Method of Moments.

Greene, William, Econometric Analysis, Prentice Hall

Lectures and practice sections. The practice sessions are designed to cover more deeply the subjects introduced in class. The numerical/statistical software MATLAB will be extensively used in these sessions.

- First exam in May 2024: Project work + 3 weekly assignments (both to be done in groups) - 70% (50% + 20%) of the final grade + 2 hours final written exam with 1 theory question and 1 exercise in MATLAB - 30% of the final grade The project work (to be handed in a week before the written exam) will be orally presented by the group members before the written exam - Exam in June 2024 and following dates: 3 hours written exam with 2 theory questions and 1 exercise in MATLAB - 100% of the final grade No carry over of the grades from project works and weekly assignments after May 2024

Assignments of the project works will be discussed with the teacher and the teaching assistant.