| Unit name | MRes Econometrics 1 |
|---|---|
| Unit code | EFIMM0021 |
| Credit points | 15 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. David Pacini |
| Open unit status | Not open |
| Pre-requisites |
None |
| Co-requisites |
MRes Mathematics for Economics |
| School/department | School of Economics |
| Faculty | Faculty of Social Sciences and Law |
This unit aims to broaden and deepen knowledge and understanding of basic econometrics. Topics will include the general linear regression model, asymptotic distribution theory, instrumental variables estimation and maximum likelihood estimation. In addition, the course will introduce and make extensive use of 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.
This unit provides a thorough and in-depth treatment of the 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.
Teaching will be delivered through a combination of synchronous and asynchronous sessions such as online teaching for large and small group, face-to-face small group classes (where possible) and interactive learning activities
Online exam 100%
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.
J. Wooldridge, Econometric Analysis of Cross Section and Panel Data, MIT
Press.
