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Course description
  ADVANCED ECONOMETRICS

Competencies and objectives

 

Course context for academic year 2022-23

This is an elective course in the second year of the Master in Quantitative Economics. Students have already taken a sequence of mandatory courses in Statistics and Econometrics in the previous year. This course presents advanced topics related to that sequence, with an special focus on managing and analysing massive data sets ("big data") and using machine learning techniques.

 

 

Course content (verified by ANECA in official undergraduate and Master’s degrees)

General Competences (CG)

  • CG1 : Capacity to carry out research work.
  • CG2 : Capacity to find data (natural and experimental) and analyse it.
  • CG3 : Capacity to apply economic theory to represent real situations.
  • CG4 : Capacity for teamwork.
  • CG5 : Capacity for self-learning.
  • CG6 : Ethical commitment and social responsibility at work, respecting the environment, being aware and understanding the importance of respect for fundamental rights, equal opportunities for men and women, universal accessibility for the disabled and respect for the values of a peaceful, democratic society.
  • CG7 : Analyse problems using critical reasoning, without prejudice and with precision and rigor.
  • CG8 : Capacity for synthesis.

 

Specific Competences (CE)

  • CE1 : Capacity to read Economic research articles in a reasoned fashion and evaluate them critically, understand their essential contributions and weaknesses.
  • CE2 : Capacity to understand how the technical problems faced by authors of research articles have been resolved in each case.
  • CE3 : Capacity to test theorems and propositions.
  • CE4 : Capacity to understand and reproduce empirical analyses and simulation experiments on which the conclusions of research articles written by other authors are based.
  • CE5 : Capacity to present important economic problems precisely and respond adequately to said problems by using the techniques learnt on the different courses, using theoretical and empirical analyses or simulations if necessary.

 

 

 

Learning outcomes (Training objectives)

No data

 

 

Specific objectives stated by the academic staff for academic year 2022-23


The purpose of this course is to show students how to manage and analyse massive data sets ("big data") and how to use machine learning techniques.

 

 

General

Code: 41242
Lecturer responsible:
ALBARRAN PEREZ, PEDRO
Credits ECTS: 5,00
Theoretical credits: 1,20
Practical credits: 0,40
Distance-base hours: 3,40

Departments involved

  • Dept: FOUNDATIONS OF ECONOMIC ANALYSIS
    Area: FOUNDATIONS OF ECONOMIC ANALYSIS
    Theoretical credits: 1,2
    Practical credits: 0,4
    This Dept. is responsible for the course.
    This Dept. is responsible for the final mark record.

Study programmes where this course is taught