This is a mathematically rigorous, theoretical course on the main econometric tools needed to read and write research papers in economics. The course covers leading methods for formulating econometric models, estimating their parameters and performing statistical inference. The techniques are specialized according to different types of data, including cross-sectional and time-series data. The course makes extensive use of linear/matrix algebra, statistical inference and some multivariable calculus. Since this is primarily a theoretical course, the assumption is that students have some training in undergraduate econometrics to provide them with motivation and examples.
Compared to the econometrics core course in the MSc Economic Policy, the focus is more on formal mathematical derivations and analytical rigor, rather than on examples of applications of the techniques.
1. The linear regression model
2. Statistical inference and asymptotic distribution theory
3. Simultaneous equations and Instrumental Variable estimation
4. Maximum Likelihood estimation
5. Generalized Method of Moments estimation
6. Elements of Time Series
Topics include: ARMA models; Unit Root testing; Vector Autoregression; Impulse-response analysis, Cointegration
Module information video
Presented by Martin Weidner
|Taught by:||Martin Weidner|
|Assessment:||Three hours of lectures per week, one practical demonstration lecture, and one weekly tutorial class throughout Term 1. Three-hour closed-book examination in Term 3. Assessment is based solely on the final examination, but a serious attempt at the problems is essential to a good understanding of the material.|
|Suitable for:||Graduate Students|
|Prerequisites:||Economics Department Approval|