PD:Econometria

Teaching plan for the course unit

Català

English

General information

Course unit name: Econometrics

Course unit code: 364566

Academic year: 2025-2026

Coordinator: Antonio Di Paolo

Department: Department of Econometrics, Statistics and Applied Economics

Credits: 6

Single program: S

Estimated learning time

Total number of hours 150

Face-to-face and/or online activities

	- Lecture with practical component	Face-to-face		45
	- Problem-solving class	Face-to-face		15

Supervised project

Independent learning

Recommendations

It is highly recommended that students enrolled in Econometrics have assimilated the content and passed the exams of the subjects Data Analysis, Statistics and Mathematics.

Competences / Learning outcomes to be gained during study

-	CB3 - Ability to gather and interpret relevant data (usually within the field of study) to inform judgements that include reflection on relevant social, scientific or ethical issues.
-	CG5 - Ability to work in a team (capacity to collaborate with others and contribute to a common project, capacity to work in cross-disciplinary and multicultural teams).
-	CG8 - Capacity to communicate in English and/or other foreign languages orally and in writing, comprehension skills, and mastery of specialized language.
-	CG1 - Commitment to ethical practice (critical and self-critical skills and attitudes that comply with ethical and deontological principles).
-	CG3 - Capacity for learning and responsibility (capacity for analysis, synthesis, to adopt global perspectove and to apply knowledge in practice).
-	CG2 - Ability to detect inequalities between people and to design, implement and evaluate policies that facilitate the erradication of discrimination on these grounds on companies and institutions.
-	CG10 - Capacity to apply ICTs to professional activities.

-	CE9 - Ability to use quantitative methods to solve real problems in different business areas.
-	CE2 - Comprehensive understanding of the international economic, legal and socio-political framework, and ability to use this knowledge to oversee international business decisions.
-	CE4 - Knowledge of international economic institutions and understanding of their role in the context of international economic relations.

Learning objectives

Referring to knowledge

The subject of Econometrics is aimed at providing basic knowledge of the simple and multiple linear regression models and their practical application as tools for analysing economic variables and gauging the existing relationships between them.

During the course, students learn how to specify and estimate multivariate regression analysis, the way in which specific hypotheses about the model to be estimated can be tested and the usefulness of multivariate regression to predict the behaviour of the variables of interest.

Subsequently, the main problems and limitations of the multivariate regression model are analysed, with the purpose of stimulating the critical perspective in analysing economic relationships through statistical methods.

Finally, some extensions of the basic multivariate regression model are presented.

Referring to abilities, skills

This course is conceived to have an applied nature, which means that the presentation of the methodological content is paralleled with practical applications using real data. Practical examples are carried out with open-source econometric software (R, R-Commander, GRETL). This is particularly important in order to promote the acquisition of practical skills such as problem solving and data management and analysis.

Throughout the course, students also attend practical sessions in the computer rooms, which are useful for improving their teamwork abilities.

Moreover, the use of statistical software to carry out regression analysis encourages the students’ ICT skills, as well as problem-solving and data analysis competences that will be useful during their professional life.

Teaching blocks

1. Simple and multiple linear regression analysis: estimation and inference

*
• Simple linear regression: estimation and interpretation
• Multiple linear regression: estimation and interpretation
• Statistical inference with the linear regression model

2. Data issues in regression analysis: specification, outliers and multicollinearity

*
• Model’s specification and selection
• Analysis of outliers
• Analysis of multicollinearity issues

3. Multiple regression analysis with qualitative information: dummy variables

*
• Single dummy variable
• Multiple dummy variables for categorical explanatory variables
• Additive and multiplicative specifications

4. Heteroscedasticity

*
• Detecting heteroscedasticity: graphical analysis
• Detecting heteroscedasticity: statistical tests
• Correcting for heteroscedasticity: robust standard errors

5. Introduction to binary choice models: LPM, logit and probit

*
• Linear Probability Model (LPM)
• The Logit Model
• The Probit Model
• Goodness of fit and interpretation of Logit and Probit models

6. Introduction to time series analysis: autocorrelation, dynamic models and forecasting techniques (ARIMA)

*
• Detecting and correcting for autocorrelation in regression with time series data
• Dynamic linear models: specification, estimation and interpretation
• Forecasting techniques: ARIMA models for time series data

Teaching methods and general organization

This course combines face-to-face lectures with sessions for smaller groups where practical problems will be solved, following the schedule that is presented below:

— 3 hours of theoretical activities with a practical component per week. The lecturer presents the theoretical and practical content of the subject, which students should assimilate in order to achieve the general and specific learning objectives of the course.

— 1 hour of practical activities per week is employed to solve practical problems. These practical sessions are conceived for small groups, with the aim of favouring the student’s learning and problem-solving abilities.

The tools of the Virtual Campus are used intensively with the purpose of facilitating communication between the lecturer and students. All the relevant academic and administrative information such as the teaching plan, the schedule of activities, the list of exercises to be solved in practical sessions and a complementary list of problems with solutions will be published on the Virtual Campus.

Official assessment of learning outcomes

Students are allowed to choose to be assessed through the continuous assessment procedure or through the single assessment procedure, although opting for the former is highly recommended.

Continuous assessment

Continuous assessment consists of:

— A battery of activities to be assessed throughout the course, which include two previously scheduled tests about the theoretical and practical material that has been explained during lectures (which account for 25% of the final grade) and exercises to be solved in groups during practical sessions (which account for 25% of the final grade). The assessment of the exercises carried out in groups is based both on the assessment of the lecturer and on peer evaluations (i.e., the average grades assigned to each student by the other members of the group). The exact weight assigned to each component will be notified to the students at the beginning of the course through the Virtual Campus.

— A final examination on the whole content of the subject, which takes place on the date established by the Academic Council and is worth 50% of the final grade.

Students who obtain a grade below 5 out of 10 in the final examination are awarded a grade of Fail for the subject (regardless of the resulting weighted average of the grades obtained in the continuous assessment activities and the final exam). Those who get a final grade of 5 out of 10 or higher pass the subject (provided that the final exam mark is higher than 5).

Students must attend at least 80% of the practical activities in order to be eligible for continuous assessment.

Repeat assessment examination

The repeat assessment examination has the same characteristics as that of the single assessment procedure (see below).

Examination-based assessment

The single assessment procedure consists of a final examination on the whole content of the subject, which takes place on the date established by the Academic Council.

Students must obtain a grade of 5 out of 10 or higher to pass the exam.

Reading and study resources

Check availability in Cercabib

Book

DOUTHERTY, Christopher. Introduction to econometrics. 5th ed. Oxford : Oxford University Press, 2016

Catàleg UB

KLEIBER, Christian., ZEILEIS, Achin. Applied econometrics with R. New York : Springer, 2008

Catàleg UB

STOCK, James H., WATSON, Marc W. Introduction to econometrics. 3rd ed. Boston : Pearson, cop. 2015

Catàleg UB
Versió en línia. Castellà (3a ed., 2012)

STUDENMUND, AH. Using econometrics : a practical guide. 7th ed. Upper Saddle River, N.J. [u.a.] : Pearson Education, 2017

Catàleg UB

WOOLDRIDGE, JM. Introductory econometrics : a modern approach. 5th ed. Melbourne : South-Western Cengage Learning, 2013

Catàleg UB

Electronic text

ADKINS, L. C. Using gretl for Principles of Econometrics, 5th. edition. [Version 1.3]. Oklahoma: Oklahoma State University, 2018 [consulta: 29 de maig de 2023]

Recurs electrònic extern