Plan docente

Econometrics I

Index

General Information

Estimated learning time

Competences / Learning outcomes

Learning objectives

Teaching blocks

Teaching methods and general organization

Official assessment of learning outcomes

Reading and study resources

General Information

Course unit name

Econometrics I

Course unit code

570051

Academic year

2024/2025

Coordinator

Rosina Moreno Serrano

Department

Department of Econometrics, Statistics and Applied Economics

Credits

Single program

Prior considerations

Estimated learning time

Total number of hours : 125 Hours

Activities	Type of training	Hours	Observations
Face-to-face and/or online activities		40
- Lecture	Face-to-face	20
- Practical exercises	Face-to-face	5
- Document study	Face-to-face	5
- Student presentation and discussion	Face-to-face	7.5
- Other class types	Face-to-face	2.5	Examination
Supervised project		42.5
Independent learning		42.5

Competences / Learning outcomes to be gained during study

Basic competences

— Ability to acquire the knowledge that fosters originality of thought in the development or application of ideas, above all in a research context.
— Ability to apply the knowledge acquired to problem solving in new or relatively unknown environments within broader (or multidisciplinary) contexts related to the field of study.

General competences

— Ability to formulate original research questions and hypotheses within a relevant theoretical framework that can be tested empirically.

— Ability to read and assess research papers in economics critically, and to identify their essential contributions to advancing the frontier of knowledge.

— Ability to write scientific articles that can be published in journals of economics and ability to present corresponding papers at international scientific conferences.

— Ability to produce financial reports for public or private organizations and communicate results clearly and precisely.

— Ability to communicate results orally and in writing in a clear and precise manner.

Specific competences

— Ability to apply economic theory and knowledge to the resolution of specific economic problems, in both new and multidisciplinary contexts.

— Ability to derive sound policy implications from empirical analyses in the area of economics.

— Ability to select and implement the econometric methodologies and techniques, databases and software tools best suited to the systematic, rigorous analysis of economic problems.

Learning objectives

Referring to knowledge

Econometrics I has one primary goal: to introduce students to Econometrics, its practical utility in the economic environment and its main limitations. It also has the specific aim of ensuring students acquire a basic understanding of one of the key elements in Econometrics: the multiple linear regression model (MLRM). To this end, students acquire an understanding of the basic assumptions of the MLRM, its main implications and properties, and some of the problems associated with its use. On completion of the course, students should therefore be familiar with the MLRM and its basic assumptions and be able to propose a simple econometrics model inspired by a problem in economics, estimate it and interpret the results obtained (i.e. from both an economic and statistical perspective).

Learning objectives

Referring to knowledge

Teaching blocks

1 Section I: Introduction

2 Section II: Multiple linear regression model (MLRM)

3 Section III: Sample problems and errors of specification

4 Section IV: Empirical econometrics

5 .

Teaching methods and general organization

Econometrics I is characterised by its eminently applied nature. As such, the teaching combines lectures, practical classes, and class presentations.

Lectures generally take place in the classroom using the projector.

Practical classes are held in the computer lab. It is essential that students put into practice the theoretical knowledge presented and so they are instructed in the use of econometrics software (specifically, Stata). The practical classes (5 in total) consist of supervised sessions in which students complete exercises on their own, and the class teacher responds to any doubts or problems that might arise.

Students are required to present (individually or in groups) an assignment based on an estimated regression model.

All the lectures and practical exercises from the course are available on the Virtual Campus.

Official assessment of learning outcomes

Continuous assessment

The final grade is based on the following:

1. The mark obtained on a mid-term exam and the exercises completed during the semester: 10% of the final grade.

2. Class presentation of an empirical paper (completed in groups or individually): 25% of the final grade.

3. Final written examination: 65% of the final grade.

Single assessment

A) An empirical paper completed individually (to be submitted on the day of the final examination and evaluated together with this exam). If deemed necessary, students may have to answer questions on the paper they submit at a later date. 20% of the final grade.

B) Final written examination. 80% of the final grade.

To pass the course, students must obtain at least 5 points out of 10 on this final exam.