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Understanding and using econometric techniques at a masters levels.
Indicative Module Content:1. Linear Regression (Ch. 2)
- model, OLS estimator
- Gauss-Markov assumptions, small sample properties, hypothesis testing
- asymptotic properties
2. More on the Linear Model (Ch. 2-3)
- missing data, outliers
- multicollinearity
- selecting regressors
- selecting functional form
3. Heteroskedasticity (Ch. 4)
4. Autocorrelation (Ch. 4)
5. Endogeneity (Ch. 5)
- Instrumental Variables estimator
- 2-Stage-Least-Squares and Generalized IV estimator
- Generalized Method of Moments
6. Maximum Likelihood (Ch. 6)
- introduction and computational issues
- specification tests: LR, Wald and LM tests
- tests for: omitted variables, heteroskedasticity and autocorrelation
Student Effort Type | Hours |
---|---|
Lectures | 30 |
Computer Aided Lab | 20 |
Autonomous Student Learning | 100 |
Total | 150 |
Students must have a sound knowledge of matrix algebra and basic statistical concepts (random variables, expectation, common probability distribution - normal, chi square, t and F, joint distributions, point estimation and inference, interval estimation).
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Students will be assigned data to analyse & write-up. They may, if they choose, work in groups of up to two (2) people. | Week 11 | n/a | Graded | No | 20 |
Class Test: Computer lab test: students will be given a dataset and asked to perform empirical analysis. | Unspecified | n/a | Graded | No | 30 |
Examination: Final exam | 2 hour End of Trimester Exam | No | Graded | No | 30 |
Examination: Midterm Exam | Week 7 | No | Graded | No | 20 |
Resit In | Terminal Exam |
---|---|
Summer | No |
• Group/class feedback, post-assessment
• Self-assessment activities
1. Regular problem sets will be assigned throughout the semester for self-assessment; solutions will be posted on Brightspace and will be explained in detail during tutorials 2. General feedback will be provided to the class.
Lecture | Offering 1 | Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 | Tues 10:00 - 11:50 |
Lecture | Offering 1 | Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 | Wed 14:00 - 14:50 |
Computer Aided Lab | Offering 1 | Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 | Fri 10:00 - 11:50 |
Computer Aided Lab | Offering 2 | Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 | Fri 12:00 - 13:50 |