| Unit name | MRes Econometrics 2 |
|---|---|
| Unit code | EFIMM0022 |
| Credit points | 15 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Professor. Gerard van den Berg |
| Open unit status | Not open |
| Pre-requisites |
MRes Econometrics I |
| Co-requisites |
None |
| School/department | School of Economics |
| Faculty | Faculty of Social Sciences and Law |
This unit aims to continue the broadening and deepening of knowledge and understanding of basic econometrics begun in MRes Econometrics I. The course will cover topics in Time Series Econometrics and Microeconometrics. The time series topics include ARMA models, VAR models and unit roots, cointegration and error correction models. The Microeconometrics topics include dynamic panel data models, quantile regression and causal inference methods for treatment effects estimation. In addition, the course will introduce and make extensive use of further linear/matrix algebra, differential calculus and statistical inference techniques. The unit aims to build in students the ability to know, understand, and evaluate these tools and to apply them when undertaking novel research. Applications will highlight the scope and limitations of these tools.
The objectives are to follow on from MRes Econometrics I in providing a thorough and in-depth treatment of further basic concepts in econometrics and introduces fundamental analytic paradigms rigorously, with a view to equip the students with sufficient foundational understanding of the discipline to be able to access the journal articles first-hand, to evaluate them critically and to start independent research projects at basic levels. Students will also learn to the application of statistical software to these tools, its scope and limitations.
There are two lectures and one exercise class per week. Coursework will consist of weekly exercises which will be used for course assessment.
Lectures will introduce and explain the different concepts and methods as well as their application and limitation whereas exercise classes will provide the opportunity to practice the selection and use of these methods as well as the application, scope and limitation of statistical software.
Contact Hours Per Week 3
Student Input
20 hours lectures
10 hours tutorials
15 hours preparation of weekly exercises for assessment
3 hours final exam
102 hours individual study
Summative assessment: 3-hour written exam (85%), weekly exercises on the various topics (15%). The exam will test the knowledge, selection, application and evaluation of tools and methods, whereas the exercises will incentivize the students to learn to use, apply and evaluate these methods as well as computational software while getting feedback on their progress.
Formative assessment: class participation and discussion in tutorials. These will provide further opportunities for feedback on the students’ progress.
R. Davidson & J.G. MacKinnon, Econometric Theory and Methods, OUP
W. Greene, Econometric Analysis, (Seventh Ed.), Prentice Hall
P. Ruud, An Introduction to Classical Econometric Theory, OUP
M. Verbeek, A Guide to Modern Econometrics, (Fourth Ed.), J. Wiley and Sons.
A.C. Cameron and P.K. Trivedi, Microeconometrics, Methods and Applications, Cambridge University Press
J.D. Hamilton, Time Series Analysis, Princeton University Press
J.M. Wooldridge J.M., Econometric Analysis of Cross Section and Panel Data, (Second Ed.), MIT Press
