23908 Advanced Econometrics
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Credit points: 6 cp
Subject level:
Undergraduate
Result type: Grade and marksRequisite(s): 23917c Advanced Macroeconomics AND 23909c Thesis Proposal in Economics (Honours) AND 23907c Advanced Microeconomics
The lower case 'c' after the subject code indicates that the subject is a corequisite. See definitions for details.
These requisites may not apply to students in certain courses.
There are course requisites for this subject. See access conditions.
Description
This subject covers advanced econometrics and its empirical application at honours and doctoral level.
Subject learning objectives (SLOs)
| 1. | understand basic statistical properties of linear regression and other estimators |
|---|---|
| 2. | understand and apply methods for dealing with different data (cross section as well as panel data, discrete choice or count data) |
| 3. | understand and apply methods for policy evaluation and estimation of parameters that are subject to endogeneity bias |
Contribution to the development of graduate attributes
This subject develops advanced econometric treatment at the honours and PhD level. The course covers advanced techniques that are used in modern empirical research. During the discussion of estimation strategies, concrete empirical examples are provided.
This subject also contributes specifically to develop the following Program Learning Objectives:
- Apply the appropriate research method and analytical tools in addressing discipline specific problems (4.1)
Teaching and learning strategies
The subject will be taught using a combination of lectures and tutorials, which may include computer lab sessions. Tutorial sessions will include instruction on the use of econometric software packages, such as STATA, MATLAB, etc.
Content (topics)
The content will be selected from the following areas:
- Foundations of statistics and econometrics.
- Estimation of linear or nonlinear models, which may include instrumental variables, GMM, Maximum Likelihood, etc.
- Panel data models and limited dependent variable models.
- Methods for policy evaluation. Endogeneity issues and the methods to deal with them.
Assessment
Assessment task 1: Assignment (Individual)
| Objective(s): | This addresses subject learning objective(s): 1 and 3 |
|---|---|
| Weight: | 25% |
Assessment task 2: Assignment (Individual)*
| Objective(s): | This addresses subject learning objective(s): 2 and 3 |
|---|---|
| Weight: | 25% |
| Criteria: | *Note: Late submission of the assessment task will not be marked and awarded a mark of zero. |
Assessment task 3: Final examination (Individual)
| Objective(s): | This addresses subject learning objective(s): 1, 2 and 3 |
|---|---|
| Weight: | 50% |
Minimum requirements
Students must achieve at least 50% of the subject’s total marks.
Required texts
There is no single textbook that covers all the topics discussed in this subject. Nevertheless, most of the lectures are based on a combination of
Joshua D. Angrist and Jorn-Steffen Pischke: "Mastering ‘Metrics: The Path from Cause to Effect", Princeton University Press, 2014 [to give you an intuition of the econometric approach]
Joshua D. Angrist and Jorn-Steffen Pischke: "Mostly Harmless Econometrics", Princeton University Press, 2008. [to give you a more formal description of the approach]
Recommended texts
Some useful reading materials are:
Scott Cunningham Causal Inference: The Mixtape is a nice book, similar in style and content to what we cover in class. There's also some R and Stata code.
James Stock and Mark Watson: "Introduction to Econometrics", 4th edition, Pearson. (this book is more at an introductory level but can be helpful if you find some concepts too advanced)
Jeff Wooldridge: "Econometric Analysis of Cross Section and Panel Data" 2nd edition. MIT Press. (this book is more formal but very good if you want to focus on the properties of an estimator)