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Curricular information is subject to change
By the end of this course, you should be able to:
• Assess critically the empirical methods used in the analysis of micro data sets
• Apply the appropriate econometric techniques to own research
• Interpret econometric output from software packages
• Theoretically derive linear and non-linear estimators
• Communicate research results appropriately in written and oral formats
The course covers the following topics:
OLS estimation
Statistical inference (including simulation-based approaches)
Fixed effect models
Matching
Differences-in-differences
Time permitting: Quantile regression, Discrete choice, GMM
Student Effort Type | Hours |
---|---|
Autonomous Student Learning | 175 |
Lectures | 24 |
Total | 199 |
This course requires completion of a M.Sc-level course in econometrics and/or statistics. Students should be familiar with matrix algebra.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Referee Report & presentation | Varies over the Trimester | n/a | Graded | No | 25 |
Continuous Assessment: Problem sets | Throughout the Trimester | n/a | Graded | No | 24 |
Examination: Final exam - end of semester | Unspecified | No | Graded | No | 21 |
Assignment: Replication exercise | Varies over the Trimester | n/a | Graded | No | 30 |
Resit In | Terminal Exam |
---|---|
Spring | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Dr Benjamin Elsner | Tutor |
Giacomo lo Conte | Tutor |