| 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 econometrics begun in MRes Econometrics I. The unit will focus on microeconometrics, that is, the econometric analysis of samples of heterogeneous subjects (individuals, firms, households, schools etc.). Part of the unit covers inference using non-linear models, and part deals with the econometric evaluation of treatment effects and policy effects. The objective is to provide a thorough and in-depth discussion of the relevant concepts and to introduce fundamental analytic paradigms rigorously. The non-linear models that are discussed include duration (or survival) models, binary outcome models, count data models, and models with truncation or censoring. The evaluation of treatment effects focuses on dynamic settings. We discuss effects of hazard rates, propensity score methods based on conditional independence assumptions, and difference-in-differences. We build on the knowledge of panel data models and instrumental variable estimation from MRes Econometrics I. Applications will focus on unemployment durations, effects of labour market policy, training and longevity.
1. The unit aims to build in students the ability to know, understand, and evaluate the various econometric tools and to apply them when undertaking novel research. Different methods invariably rely on different sets of assumptions, and after following the unit the student should be able to assess the plausibility of such assumptions before applying a method.
2. Moreover, the student must be able to grasp the intuition behind the workings of particular econometric methods, in order to assess their appeal and/or limitations and to develop ideas for methodological improvements.
3. The students should obtain sufficient foundational understanding of the topics to be able to access the corresponding journal articles first-hand and to evaluate them critically.
4. They should be able to start independent research projects with microeconometric data that require the methods taught, at basic levels.
5. Students will also learn the application of statistical software to these tools.
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
Coursework (15%) and Onlline exam (85%)
Wooldridge J.M. (2010), Econometric Analysis of Cross Section and Panel Data, (Fifth Ed.), MIT Press
Van den Berg, G.J. (2001), Duration models: specification, identification, and multiple durations, in: J.J. Heckman and E. Leamer, eds. Handbook of Econometrics, Volume V, North-Holland
Cameron, A.C. and P.K. Trivedi (2005), Microeconometrics, Methods and Applications, Cambridge University Press
Lancaster, T. (1990), Econometric Duration Analysis, Cambridge University Press
Greene, W. (2012), Econometric Analysis, (Seventh Ed.), Prentice Hall
