Guías Académicas

ECONOMIC ANALYSIS OF DATA

ECONOMIC ANALYSIS OF DATA

GRADO ECONOMÍA

Curso 2022/2023

1. Datos de la asignatura

(Fecha última modificación: 07-05-22 19:13)
Código
103754
Plan
ECTS
6.00
Carácter
OPTATIVA
Curso
4
Periodicidad
Primer Semestre
Idioma
Castellano
Área
FUNDAMENTOS DEL ANÁLISIS ECONÓMICO
Departamento
Economía e Historia Económica
Plataforma Virtual

Campus Virtual de la Universidad de Salamanca

Datos del profesorado

Coordinador/Coordinadora
Rebeca Jiménez Rodríguez
Grupo/s
Único
Centro
Fac. Economía y Empresa
Departamento
Economía e Historia Económica
Área
Fundamentos del Análisis Económico
Despacho
206 Edificio FES
Horario de tutorías
TBA
URL Web
https://diarium.usal.es/rebecajimenez/
E-mail
rebeca.jimenez@usal.es
Teléfono
923294500 (Ext. 4668)

2. Sentido de la materia en el plan de estudios

Bloque formativo al que pertenece la materia.

 Quantitative Methods

Papel de la asignatura.

This course contributes to the acquisition of specific and cross-curricular skills of the Module "Quantitative Methods".

Perfil profesional.

Economist.

3. Recomendaciones previas

Students should possess a solid background in Algebra, Mathematical Analysis, Statistics I, Statistics II, Macroeconomics I, Macroeconomics II, Microeconomics I, Microeconomics II, Microeconomics III, Econometrics I and Econometrics II.

 

It is also advisable to have basic computer skills (Windows environment).

The course is taught in English and, consequently, students should have a good command of English (minimum level: B1).

4. Objetivo de la asignatura

Learning outcomes:

 

- Carrying out applications of econometric techniques by themselves, as well as being initiated into the realization of empirical investigations.

- Use of the following econometric softwares: EViews and Stata.

- Choosing the most suitable econometric techniques between those known for data analysis in a particular case.

- Analyzing the results from an economic perspective, maintaining a critical sense when evaluating the uncertainty associated to the results.

5. Contenidos

Teoría.

- Empirical applications of techniques for econometric modelling of limited dependent variables and panel data.

- Empirical applications of techniques for econometric modelling of time series data.

6. Competencias a adquirir

Básicas / Generales.

The skills to be developed in this course contribute to the student acquiring those established in the Module “Quantitative Methods” (skills that are included in the Report of the Bachelor’s Degree in Economics).

Specifically, the following skills are developed:


 

Basic/General skills

- Learning to apply econometric methods in an empirical context.

- Ability to judge the results of the application of quantitative methods in data analysis.

- Training to discern between alternative methods of analysis.

- Competency in the use of statistical and econometric software.

- A certain degree of development of their capacity to carry out their own empirical research.

Específicas.

- Developing the ability to choose the most suitable econometric techniques between those known for data analysis in a particular case. It covers competence CE.4 of the Bachelor’s Degree in Economics.

- Learning to apply the econometric methods studied in an empirical context, maintaining a critical sense when assessing the uncertainty associated to the results. It covers competence CE.3 of the Bachelor’s Degree in Economics.

- Ability to judge the results of econometric analyses obtained by themselves, and/or to start conducting empirical researches leading to those results. It covers competences CE5, CE.10, CE.12 and CE.17 of the Bachelor’s Degree in Economics.

- Competency to read and communicate effectively in English within a professional environment. It covers competence CE. 14 of the Bachelor’s Degree in Economics.

Transversales.

- Autonomous learning ability. It covers competence C.23 of the Bachelor’s Degree in Economics.

- Adaptability to new situations. It covers competence C.24 of the Bachelor’s Degree in Economics.

- Ability to develop scientific criticism and self-criticism. It covers competence C.25 of the Bachelor’s Degree in Economics

7. Metodologías

  • Activities under teacher supervision:
    • Master Sessions:
      •   Introductory activities
      •    Theoretical activities

    • Practical activities: 
      • In the classroom
      • In the computer classroom
    • Specific tutoring
    • Presentations and debates                                                                             
  • Autonomous practical activities (without teacher supervision):
    • Complementary readings.
    • Performing and analyzing empirical examples.
    • Making theoretical demonstrations.
    • Preparation of an essay/paper.     
    • Conducting research and preparing presentations.
  • Evaluation tests.

8. Previsión de Técnicas (Estrategias) Docentes

9. Recursos

Libros de consulta para el alumno.

Hamilton, J. (1994). Time Series Analysis. Princeton.

Hsiao, C. (2007). Analysis of Panel Data. Cambridge University Press.

Matilla-García, M., Pérez-Pascual,  P. y B. Sanz-Carnero (2017), Econometría y predicción. McGraw-Hill.

Wooldridge, J.M. (2010). Econometric Analysis of Cross Section and Panel Data Modeling. The MIT Press.

Wooldridge, J.M. (2013), Introducción a la Econometría: Un Enfoque Moderno. 5ª Edición. CENGAGE Learning.

Verbeek, M. (2012). A Guide to Modern Econometrics.  Wiley. UK.

Otras referencias bibliográficas, electrónicas o cualquier otro tipo de recurso.

Baltagi, B.H. (2013), Econometric analysis of panel data. Wiley. 5th edition.

Software EViews (program for the econometric analysis of economic data installed in the computer rooms).

Software Stata (program for the econometric analysis of economic data installed in the computer rooms).

10. Evaluación

Consideraciones generales.

The course requires an average dedication of 150 hours (6 ECTS): 45 hours (30%) correspond to face-to-face dedication or mandatory interaction with the teacher and 105 hours (70%) correspond to autonomous work.

Criterios de evaluación.

Assessment criteria:

  • 40% of the final grade (continuous assessment grade): evaluation of the student work during the course.
  • 60% of the final grade: writing an essay/paper and giving a final presentation or alternatively doing a final exam.

To pass the course, a definitive grade equal to or higher than 5 (out of 10) is required, without a minimum of grade in the continuous assessment or in the performance of the essay/paper and its presentation or alternatively in the final exam (but always the sum of both must be equal to or higher than 5).

The assessment criteria will be the same in the ordinary call and the extraordinary call. Since the continuous assessment requires a follow-up throughout the course, you cannot resit this part in the extraordinary call or in the calls corresponding to the early evaluation.

Class attendance is mandatory and, consequently, is not part of the final grade.

The student must have fulfilled 80% of their face-to-face dedication so that their work is considered part of the final grade.

Instrumentos de evaluación.

- Questionnaires.

- In-class assessment.

- Essay/paper.

- Presentations.

- Final exam (if necessary).

Recomendaciones para la evaluación.

It is recommended that the student strives to pass the continuous assessment.

Recomendaciones para la recuperación.

The grade obtained in the continuous assessment cannot be resat and will be maintained for all the calls that are made during the corresponding academic year, as well as for the call for early evaluation corresponding to the next academic year or the successive ones.