Checking date: 24/10/2019


Course: 2019/2020

Econometrics for Finance
(15908)
Study: Master in Finance (261)
EPE


Coordinating teacher: GONZALO MUÑOZ, JESUS

Department assigned to the subject: Department of Economics

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course:
Semester:




Students are expected to have completed
Target Audience: This course is adequate for any student in the MSc in Finance. A basic understanding of probability theory and of the properties of the conditional expectation is assumed. Course Language: English. The course of Financial Statistics (First Term) should have been completed previously. Computer exercises will be done using Eviews. **Professor: Antonio Rubia (Associate Professor Universidad de Alicante)
Competences and skills that will be acquired and learning results.
This course aims at providing the student with basic econometric skills used in empirical economic research. This goal will be accomplished through classroom lectures, practical sessions, and problem sets. Specifically, by the end of the course the student should be able to: 1) Apply basic linear regression techniques in economic problems. 2) Use appropriate software (Eviews) to implement quantitative research. Skills the student will be able to gain during the course are: 1) Understanding data limitations and their consequences in empirical analysis. 2) Understanding the merits of alternative quantitative methods. 3) Interpreting results in terms of policy implications and prediction purposes.
Description of contents: programme
Part I: Linear regression models 1.1 Introduction. 1.2 Preliminaries. 1.3 Parameter estimation. 1.4 Regression analysis. 1.5 Departures from the classical assumptions. 1.6 Statistical inference in the linear regression model. Part II: Panel data analysis 2.1 Introduction. 2.2 Preliminaries. 2.3 Pooled OLS estimator. 2.4 Fixed effects panel estimator. 2.5 Random effects panel estimator. 2.6 Summary. Part III: Long-run relationships in Finance 3.1 Introduction. 3.2 Preliminaries. 3.3 Unit root testing. 3.4 Spurious regressions. 3.5 Error correction and VAR modelling. 3.6 System cointegration tests.
Learning activities and methodology
Learning activities will consist on lectures, computer practice sessions (illustrating the implementation of the econometrics techniques using real economic and financial data), and solving exercises from problem sets. Computer exercises will be done using Eviews. Practice is essential to learning and understanding econometric tools. Therefore, there will be computer practice sessions and also computer exercises as homework. The course will focus on how to implement basic econometric techniques. Slides and book references are provided to facilitate successful course attendance. Slides, exercises, and other materials will be available at Aula Global. The chosen software to practice with econometric tools is Eviews. No late work will be accepted. Students will also be encouraged to attend the office hours in order to receive clarification on material covered in class. Office hours will not be available for checking if answers to homework are correct: Students will be encouraged to compare answers with their classmates for this purpose.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40
Basic Bibliography
  • Brooks, C. . Introductory Econometrics for Finance. . Cambridge University Press, 4th edition. 2019
  • Greene, W. H. . Econometric Analysis, . Pearson 8th edition. 2017
  • Wooldridge, J.M. Introductory Econometrics: A Modern Approach,. 2nd ed., Thomson South-Western. 2003
Additional Bibliography
  • Stock, J. and Watson, M.. Introduction to Econometrics. Addison-Wesley. 2003

The course syllabus and the academic weekly planning may change due academic events or other reasons.