2025
Econometrics
Name: Econometrics
Code: ECN11907M
6 ECTS
Duration: 15 weeks/156 hours
Scientific Area:
Economy
Teaching languages: Portuguese
Languages of tutoring support: Portuguese, English
Regime de Frequência: Presencial
Presentation
This unit aims at developing the skills and knowledge of modern econometrics necessary for theoretical and empirical work in economics and related fields.
Sustainable Development Goals
Learning Goals
Aims:
- To develop the skills in, and knowledge of, econometrics necessary for theoretical and empirical work based on cross-sectional, time-series or panel data.
- To learn how to conduct empirical studies in economics and related fields using modern econometric techniques.
Competences:
- Data analysis and manipulation.
- Reason logically and work analytically.
- Work with abstract concepts and in a context of generality.
- Select and apply appropriate techniques to solve econometric problems.
- Team work, written and oral communication.
- Computing skills and knowledge of econometric software.
- To develop the skills in, and knowledge of, econometrics necessary for theoretical and empirical work based on cross-sectional, time-series or panel data.
- To learn how to conduct empirical studies in economics and related fields using modern econometric techniques.
Competences:
- Data analysis and manipulation.
- Reason logically and work analytically.
- Work with abstract concepts and in a context of generality.
- Select and apply appropriate techniques to solve econometric problems.
- Team work, written and oral communication.
- Computing skills and knowledge of econometric software.
Contents
I. Linear Regression Model: Specification, Estimation and Inference; Endogenous Regressors.
II. Nonlinear Models: Estimation and Inference; Models with discrete dependent variable
III. Time Series Models: Univariate and Multivariate Models; Unit roots and Cointegration.
IV. Panel Data Models: Fixed and Random Effects Models; Dynamic Models.
II. Nonlinear Models: Estimation and Inference; Models with discrete dependent variable
III. Time Series Models: Univariate and Multivariate Models; Unit roots and Cointegration.
IV. Panel Data Models: Fixed and Random Effects Models; Dynamic Models.
Teaching Methods
The teaching process is based on theoretical-practical classes and some seminar sessions with a reduced number of students. In the theoretical-practical sessions, typically, after the theoretical content is delivered, students solve exercises that help them understand the concepts and apply their knowledge. In solving practical exercises, priority is given to the use of real economic data by using appropriate econometric software. In the seminar sessions, the aim is to study applied cases and to solve real or simulated problems, for which students must use econometric tools to find solutions. In this process, the discussion of the problems proposed by the instructor is encouraged in small groups of students, and the solutions found are shared and analysed with the whole class.
Throughout the semester, Moodle is used for organising and accessing learning materials and for communication between students and the instructor, as well as for communication among the students themselves.
Students may use AI tools in this course unit as technical, analytical, and learning support, but they must do so responsibly and ethically, while maintaining academic integrity. It will be unacceptable to use AI in assessments or examinations without authorization from the instructor. Improper use of AI will be treated as academic misconduct under Article 119 of the Academic Regulations (Code of Conduct, Fraud and Plagiarism).
Throughout the semester, Moodle is used for organising and accessing learning materials and for communication between students and the instructor, as well as for communication among the students themselves.
Students may use AI tools in this course unit as technical, analytical, and learning support, but they must do so responsibly and ethically, while maintaining academic integrity. It will be unacceptable to use AI in assessments or examinations without authorization from the instructor. Improper use of AI will be treated as academic misconduct under Article 119 of the Academic Regulations (Code of Conduct, Fraud and Plagiarism).
Assessment
This course unit aims to prioritize continuous evaluation system, combining two evaluation methods in order to stimulate logical reasoning, result interpretation, and the use of statistical software. Continuous assessment consists of an individual open-book written test (weighted at 60%) and a group project involving two or three students (weighted at 40%). While the written test primarily assesses knowledge of the models and the interpretation of results, the group project evaluates the ability to apply models to real data, the critical interpretation of econometric results, as well as proficiency in using statistical software.
Alternatively, students may choose to take a written final exam weighted at 100%.
If necessary, in order to access the extent to which the student has acquired the knowledge and competences established for the course unit, the lecturer may require the student to take an oral examination, in accordance with paragraph 13 of Article 110 of the Academic Regulations of the University of Évora. The conditions set out in paragraph 4 of Article 116 and paragraph 1 of Article 118 of the same Regulations will be ensured. The oral examination will account for 50% of the final grade for the course unit.
Alternatively, students may choose to take a written final exam weighted at 100%.
If necessary, in order to access the extent to which the student has acquired the knowledge and competences established for the course unit, the lecturer may require the student to take an oral examination, in accordance with paragraph 13 of Article 110 of the Academic Regulations of the University of Évora. The conditions set out in paragraph 4 of Article 116 and paragraph 1 of Article 118 of the same Regulations will be ensured. The oral examination will account for 50% of the final grade for the course unit.
Teaching Staff
- Maria Aurora Murcho Galego [responsible]
Accreditations and Certifications
