Teaching plan for the course unit

 

 

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General information

 

Course unit name: Econometrics I

Course unit code: 366720

Academic year: 2025-2026

Coordinator: Jose Ramon Garcia Sanchis

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

60

 

-  Lecture with practical component

Face-to-face

 

45

 

-  Problem-solving class

Face-to-face

 

15

Supervised project

40

Independent learning

50

 

 

Recommendations

 

Students are recommended to have previously taken the courses in mathematics (Mathematics I and II) and statistics (Statistics for Economics and Business I and II) as this facilitates the proper understanding and application of the course content of Econometrics I.

 

 

Competences / Learning outcomes to be gained during study

 

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Commitment to ethical practice (critical and self-critical skills and attitudes consistent with ethical and deontological principles).

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Capacity to apply ICTs to professional activities.

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Ability to use physical and electronic bibliographical resources.

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Ability to apply the knowledge acquired and analytical skills for solving academic and professional problems.

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Capacity to prepare, analyse and interpret economic information.

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Ability to produce critical analyses of economic theories and models.

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Knowledge and understanding of the nature, sources and uses of economic information and of the appropriate software for processing and analysing economic data.

Learning objectives

 

Referring to knowledge

Students should acquire the ability to perform professional tasks that focus not so much on the use of econometric tools, but rather on economic issues as seen from global, individual, and sectoral perspectives, in both domestic and international markets. That said, the ability to use econometrics as a research method in the social sciences, and in economics in particular, is essential.

The aim, therefore, is not to become an expert in econometrics, but to understand the philosophy underlying econometric methodology, the key concepts involved, and the application of certain econometric techniques that may prove useful in the future. Students should acquire sufficient knowledge to collaborate effectively with econometrics specialists. For economics students, econometrics should thus be viewed as an instrument that facilitates rigorous and efficient economic analysis.

The learning objectives of this course include mastering basic econometric methods and techniques, understanding key terminology and concepts, identifying problems to which econometric procedures can be applied and applying them correctly, integrating the results of econometric analysis into decision-making processes, and appreciating the relevance of econometrics as a tool for economic inquiry. Students should also develop the capacity to collaborate productively with econometrics professionals when necessary. In short, future graduates in Economics should gain a general understanding of econometric concepts and their applications, enabling them to recognize when a problem can be addressed econometrically and to employ the appropriate quantitative methods to do so.

Econometrics I is therefore designed to cover the aspects of econometrics most likely to be of practical use to future graduates entering the labour market, providing an understanding of econometrics, its applications and its main limitations. In particular, by the end of the course, students will have developed knowledge, skills, and competencies relating to multiple linear regression models (MRLMs), including the basic assumptions and implications of MRLMs, estimation methods such as ordinary least squares (OLS) and maximum likelihood (ML), the testing of linear restrictions, and the treatment of model extensions related to sampling problems and the misspecification of deterministic components.

 

 

Teaching blocks

 

Lesson 1. Multiple linear regression models (MLRMs): Introduction

Lesson 2. MLRMs: specification, estimation and validation

Lesson 3. Prediction

Lesson 4. Linear restrictions

Lesson 5. Problems with sample information: influential observations and multicollinearity

Lesson 6. Misspecification off the deterministic assumptions in MRLMs

Lesson 7. Qualitative explanatory variables

 

 

Teaching methods and general organization

 

Given that the learning objectives require substantial practical work, the general teaching methodology combines theoretical instruction with a strong emphasis on practical application. To this end, the following types of activity are undertaken:

— On-campus learning activities: these are carried out in the classroom with the lecturer. They include theoretical lectures, lectures with a practical component (for the entire group), and problem-solving sessions. In exceptional circumstances (such as those experienced in previous academic years), alternative teaching methods may be adopted, in accordance with the regulations, guidelines, and recommendations established by the University of Barcelona. Should any such changes occur, they will be communicated to students through the usual channels (such as the Virtual Campus). A total of 60 hours are devoted to these activities. To facilitate learning, students have access to lecture slides via the Virtual Campus prior to each session.

— Independent learning activities: These include problem-solving exercises, computer sessions, and self-assessment of the knowledge and skills acquired. The practical exercises involve answering a set of questions based on real economic data using an econometric program. Students must interpret the program’s calculations and results by applying the theoretical concepts learned throughout the course. A total of 50 hours are assigned to these activities.

— Tutored or directed activities: These consist of discussion of problems to clarify doubts and provide guidance on students’ independent work. A total of 40 hours are assigned to these activities.

Whenever possible, a gender perspective is incorporated into the analyses, tools, and practical exercises. The aim is to ensure that the training aligns with the University’s equality plan.

 As part of the Teaching Quality Improvement Project implemented in the Faculty of Economics and Business (promoted by the Research, Innovation and Teaching and Learning Improvement [RIMDA] unit and the Office of the Vice-Rector for Teaching and Academic Planning), the teaching methodology used in some groups may differ slightly from that described above.Details of any such variations will be published on the Virtual Campus at the beginning of the semester.

Once students have acquired the theoretical and practical content of the subject, problem-solving activities, independent work, and tutored sessions contribute to the acquisition of such competencies as the ability to produce, analyse and interpret economic information; to critically evaluate theories and models; to identify and use appropriate data sources; and to apply suitable computer tools for data analysis. Independent work also supports the development of ICT skills, which are essential for professional growth.

 

 

Official assessment of learning outcomes

 

Continuous assessment consists of two components:

a) Continuous assessment activities. Students complete two activities, representing 40% of the final grade (20% each), which must be completed on-campus on the scheduled dates.

b) Final examination. This is an exam with short-answer questions and limited space for responses, covering both the theoretical and practical components of the subject. Students must demonstrate the knowledge and skills acquired during the course, partially developed through the previous continuous assessment activities. The examination accounts for the remaining 60% of the final grade.

All assessment components completed during the course (continuous assessment activities and final examination) are graded on a scale of 0 to 10, and the percentage weightings specified above are then applied to calculate the final grade.

A student passes the course by achieving a minimum final grade of 5, calculated as the weighted average of all activities. To be eligible for continuous assessment, a minimum mark of 3.5 out of 10 must be obtained on the final examination. Otherwise, the final grade corresponds to that of the mark obtained on the examination mark alone.

Repeat assessment consists of a final examination with the same format as that for the single mode of assessment. Dates for repeat assessment are set in the Faculty’s academic calendar and coincide with the second examination period for undergraduate studies. The date for repeat assessment examinations is established by the Academic Board.

If methodological changes described in the teaching methodology section affect the assessment system, students involved will be informed of how each activity contributes to the final grade.

 

Examination-based assessment

Students who are unable to meet the requirements for continuous assessment are entitled to opt for the single mode of assessment.

To pass the subject, students must demonstrate a sufficient level of achievement of the learning objectives in a single examination, which accounts for 100% of the final grade. This is an exam with short-answer questions and limited space for responses, covering both the theoretical and practical components of the subject. Students must demonstrate the knowledge and skills acquired as outlined in this course plan. The exam is sat on campus on the official date set by the Academic Board.

To pass the subject via this mode of assessment, students must:

a) Submit a written request indicating that they wish to waive their right to continuous assessment by the deadline announced by the Academic Board.

b) Obtain a mark of at least 5 out of 10 on the examination.

Repeat assessment adheres to the same format.

Finally, note that the assessment system for the EUS group is specified in the course programme designed for this group.