Degree | Type | Year |
---|---|---|
Applied Statistics | OB | 2 |
You can view this information at the end of this document.
It is recommended that the student have studied mathematics, statistics and linear models that have given him knowledge in linear algebra, matrix analysis, theory of probability and inference statistics (estimation and contrast of hypotheses).
This course introduces students to the empirical analysis of relationships between economic variables, providing the fundamental tools to interpret and apply econometric models in real-world contexts.
The course begins with the simple linear regression model, revisiting concepts covered in the Statistics course, and progresses to multiple regression, incorporating both quantitative and qualitative explanatory variables. The assumptions of the linear regression model will be studied in detail, and strategies to address potential violations of these assumptions will be explored. Additionally, the use of instrumental variables and binary response models, such as logit and probit models, will be introduced.
The main objective is for students to develop the ability to extract relevant information from data using the regression model, understanding its strengths and limitations with analytical rigor. Emphasis will be placed on an intuitive understanding of the theoretical foundations of econometric analysis, complemented by a strong practical orientation. Throughout the course, students will work with real data and econometric software, allowing them to apply the concepts learned to concrete problems and develop applied analytical skills.
(T: theory, S: problems or seminars, PS: preparation of problems or seminars, L: laboratories, PP: practical preparation, E: study, AA: other activities, indicate the number of hours dedicated to each activity)
Unit 1: Introduction
Unit 2: The Simple linear regression model
Unit 3: The Multiple linear regression model
Unit 4: Specification errors
Unit 5: Violation of OLS assumptions
Unit 6: Instrumental Variables and Two-Stage Estimation
Unit 7: Models with discrete dependent variable
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Laboratory practices | 45 | 1.8 | CM14, KM17, KM18, SM16, SM18, CM14 |
Theory | 30 | 1.2 | CM14, KM17, KM18, CM14 |
Type: Supervised | |||
Solving problems | 30 | 1.2 | CM14, KM17, SM18, CM14 |
Type: Autonomous | |||
Study | 45 | 1.8 | KM17, KM18, KM17 |
Two hours of theoretical classes a week plus two of practices and guided teamwork for the applied essay (with econometric software) and resolution of exercises related to the contents explained in class in order to favor the assimilation of this knowledge by the student.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Delivery of Exercises | 10% | 0 | 0 | KM17, KM18 |
Final exam | 50% | 0 | 0 | KM17, KM18 |
Group applied project | 20% | 0 | 0 | CM14, KM17, KM18, SM16, SM18 |
Midterm | 20% | 0 | 0 | CM14, KM17, KM18 |
The activities to evaluate the subject will be:
A student who has not participated in any of the described assessment activities will receive the "Not presented" qualification. If a student performs some of the assessment activities, even if it is only one, you can no longer opt for a "Not Presented".
Resit exams
Students whose final grade is below 5 may take the resit exam.
To be eligible, it is mandatory to have completed the midterm exam and submitted the practical assignment.
The final grade after the resit will be the higher of:
80% resit exam + 20% final project
100% resit exam
The practical sessions will be conducted using R Studio.
Basic knowledge of LaTeX is recommended for writing the group research paper.
Please note that this information is provisional until 30 November 2025. You can check it through this link. To consult the language you will need to enter the CODE of the subject.
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(PLAB) Practical laboratories | 1 | Spanish | second semester | afternoon |
(TE) Theory | 1 | Spanish | second semester | afternoon |