Courses Master Display 2016-2017
|Course Description||To PDF|
|Course title||Empirical Analysis I|
Alain Hecq, Bram Foubert
For more information: firstname.lastname@example.org; email@example.com
|Language of instruction||English|
It is the purpose of this course to provide an introduction to business research applications of quantitative data analysis techniques. The main focus of this course will be on building intuition for the use of the discussed econometric techniques, as well as the actual application of the techniques with real research problems and data.
Students that have completed this course will have learned the following :
- to understand the goals of the different quantitative data analysis techniques and their applicability
- to understand the link between the discussed techniques
- to know the strengths and weaknesses of the techniques
- to understand the explicit and implicit assumptions on which the techniques are based
- to select the most suitable quantitative data analysis technique for different business research problems
- to be able to apply the different quantitative data analysis techniques by using STATA
- to interpret the results of the techniques.
"EMPIRICAL ANALYSIS I" IS THE NEW TITLE FOR THE COURSE PREVIOUSLY LABELLED "APPLIED QUANTITATIVE ANALYSIS".
The course is structured around linear regression analysis, and builds from the simple linear regression model by focusing on extensions that are relevant for empirical applications. The course will be structured around the following themes: (1) Linear regression introduction, (2) Heteroscedasticity, (3) Linear regression with time series data, (4) Endogeneity including instrumental variables estimation, (5) Panel data.
The course aims at presenting the topics above at an intermediate level and allows for further specialisation either in advanced QRMB I (EBC4134), QRMB II (EBC4135), or the electives, such as for instance EBC4006.
Wooldridge, Jeffrey M. (2009), Introductory Econometrics: A Modern Approach (4th ed.), South-Western Cengage Learning.
Students participating in this course should have a basic statistical knowledge, and should be familiar with basic quantitative data analysis techniques such as linear regressions.
|Teaching methods||PBL / Presentation / Lecture / Assignment / Papers|
|Assessment methods||Oral Exam|
|Evaluation in previous academic year||For the complete evaluation of this course please click "here"|
|This course belongs to the following programmes / specialisations||