| Study programme competencies |
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Code
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Study programme competences
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| A2 |
CE2 - Conocimiento riguroso de los modelos micro y macroeconómicos y su aplicación precisa a situaciones concretas. |
| A3 |
CE3 - Manejo de las técnicas econométricas actuales. |
| A4 |
CE4 - Capacidad para modelar situaciones económicas concretas y obtener resultados con datos numéricos aplicando las técnicas econométricas pertinentes. |
| B6 |
CG1 - Aplicar los conocimientos de economía a la identificación, previsión y solución de los problemas económicos en general, y en particular los relativos al nivel de especialización, en entornos nuevos o poco conocidos. |
| B13 |
CG8 - Capacidad para entender y explicar datos económicos y para trabajar con ellos mediante las técnicas más actuales. |
| C1 |
CT1 - Capacidad para comprender el significado y aplicación de la perspectiva de género en los distintos ámbitos de conocimiento y en la práctica profesional con el objetivo de alcanzar una sociedad más justa e igualitaria. |
| C4 |
CT4 - Capacidad para interaccionar y defender con rigor, claridad y precisión ante otro especialistas trabajos, propuestas, nuevas ideas etc. |
| C5 |
CT5 - Comunicación oral e escrita. |
| C7 |
CT7 - Capacidad para comunicarse por oral y por escrito en lengua inglesa. |
| Learning aims |
| Learning outcomes |
Study programme competences |
| Ability to search, identify and interpret sources of relevant economic and financial information. Capacity for diagnosis and strategic and prospective analysis, with visión over the medium- and long-term. Capacity to process the information in a comprehensive way by incorporating it to the decisión-making process.
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AC2
AC3
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BC13
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CC1
CC4
CC5
CC7
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| Ability to work in a team. Capacity to cope with complex issues in a sistematic and creative approach, and to forward the conclusions to all the types of audiences. Adaptation capability, originality and critical spirit. |
AC3
AC4
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BC6
BC13
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CC4
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| Contents |
| Topic |
Sub-topic |
| Lesson 1.- Searching for patterns in databases |
Introduction to data mining
Introduction to multivariate analysis
Descriptive techniques and visualization of multivariate data
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| Lesson 2.- Dimensionality reduction methods |
Principal component analyis
Factorial analysis
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| Lesson 3.- Unsupervised and supervised classification |
Clustering
Discriminant analysis |
| Lesson 4.- Statistical inference: advanced techniques |
Introduction to nonparametric inference
Smoothing techniques
Nonparametric regression
Semiparametric regression
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| Practicum |
Applications using R software to study cases and practical examples. |
| Planning |
| Methodologies / tests |
Competencies |
Ordinary class hours |
Student’s personal work hours |
Total hours |
| Guest lecture / keynote speech |
A4 A2 A3 B6 B13 C1 C4 C5 |
10 |
18 |
28 |
| ICT practicals |
A3 B13 C4 C7 |
5 |
20 |
25 |
| Supervised projects |
A4 A3 B6 B13 C1 C4 C5 C7 |
0 |
20 |
20 |
| Objective test |
A4 A3 C1 C4 C5 |
1 |
0 |
1 |
| |
| Personalized attention |
|
1 |
0 |
1 |
| |
| (*)The information in the planning table is for guidance only and does not take into account the heterogeneity of the students. |
| Methodologies |
|
Methodologies |
Description |
| Guest lecture / keynote speech |
Oral expositions, with the support of audivisual material, including theoretical concepts and practical examples. |
| ICT practicals |
Supported and supervised by the instructors, the students will carry out empirical applications proposed during the course. |
| Supervised projects |
Every student, properly supervised, must complete a specific project involving real data using techniques developed throughout the course. |
| Objective test |
Final exam conducted to evaluate the capacity of the students in order to understand, interrelate and integrate the concepts and techniques developed during the course. |
| Personalized attention |
|
Methodologies
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| ICT practicals |
| Supervised projects |
|
| Description |
| Every student must complete, properly supervised, a specific project involving real data and using techniques and skills developed throughout the course. Personalized attention will consist in monitoring the different stages of the project at succesive working meetings. |
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| Assessment |
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Methodologies
|
Competencies |
Description
|
Qualification
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| ICT practicals |
A3 B13 C4 C7 |
Development of empirical applications proposed and supervised by the instructors. |
10 |
| Objective test |
A4 A3 C1 C4 C5 |
Written exam |
25 |
| Supervised projects |
A4 A3 B6 B13 C1 C4 C5 C7 |
Individual project |
65 |
| |
|
Assessment comments |
Knowledge of English is highly advisable, particularly of reading comprehension, since part of the study material and most of the references are in this language. Assessment will consist of the weighted sum of the results attained in the development of the ICT practicals (0.10), the individual project (0.65) and the written exam (0.25). Active participation in the class is also desirable. Excepcional cases: If, for duly justified reasons, the student cannot complete all the assessment tasks, then the instructor will adopt the criterion he considers appropriate for the assessment purpose. |
| Sources of information |
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Basic
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Everitt B., Hothorn T. (2011). An Introduction to Applied Multivariate Analysis with R. Springer
Peña D. (2002). Análisis de datos multivariantes. McGraw-Hill/Interamericana
Härdle W., Simar L. (2003). Applied Multivariate Statistical Analysis. Springer
Härdle W., Müller M., Sperlich S., Werwatz, A. (2004). Nonparametric and Semiparametric Models. Springer
Li Q., Racine J.S. (2006). Nonparametric Econometrics. Princeton University Press
Horowitz J.L. (2009). Semiparametric and Nonparametric Methods in Econometrics. Springer
Ruppert D., Wand M.P., Carroll R.J. (2003). Semiparametric Regression. Cambridge University Press
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Complementary
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Dalgaard P. (2002). Introductory Statistics with R. Springer
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| Recommendations |
| Subjects that it is recommended to have taken before |
| Quantitative Methods/611532004 | | Econometric Techniques/611532003 |
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| Subjects that are recommended to be taken simultaneously |
| Advanced Econometrics/611532027 |
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| Subjects that continue the syllabus |
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