By the end of the course the student is able to:
- understand the linear regression regression model;
- understand the derivation of the main estimators, such as Ordinary Least Squares, Instrumental Variables, Generalized Least Squares, Maximum Likelihood, Methods of Moments.
- understand the main statistical testing procedures that are related to these estimators, as well as their application to various misspecification tests (heteroskedasticity, autocorrelation, endogeneity, stationarity, and cointegration)
- understand specific regression models, such as limited dependent variable models, (dynamic) panel data models, time-series models (VAR; error-correction)
This course provides a thorough understanding of the main econometric techniques. Knowledge of this course allows one to understand modern empirical economic literature. The linear regression model will be considered by linear algebra (matrices, vectors) and it will be used to derive the main estimators and hypothesis tests. In addition, the properties of these estimators (e.g. bias, consistency, and efficiency) will be considered.
Format: Lectures, tutorials, and assignments.
- Entrance test in week 1 (5%)
- Midterm exam on material of week 1 – week 4 (exam in week 5) (45%).
- Emprical individual assignment to be handed in in week 7 (5%)
- Endterm exam on material of week 5 – week 8 (exam in week 9 (45%).
|It is expected that the students have knowledge of econometrics at the level of Wooldridge (2009), chapters 2, 3, 4, 7, Appendix B and C. There will be an entrance test on this material in the first week of the course. Those who fail for this test will be recommended not to continue with the course.|
Entry requirementsBasic knowledge of calculus, linear algebra, statistics, and econometrics is required.
|Verbeek, M. (2012) A Guide to Modern Econometrics, 4th edition. Edition. Chichester: Wiley. ISBN: 978-1-1199-5167-4|
|Wooldridge, J.M. (2009). Introductory Econometrics; A Modern Approach, 4thedition. South-Western College Publishing Co.|