Show/hide contentOpenClose All
Curricular information is subject to change
By the end of this course, students should be able to:
1) write down the appropriate econometric model among the ones seen in class, given a research question they are asked to address and the dataset available;
2) identify the appropriate estimator among the ones seen in class;
2) estimate all the models seen in class using STATA .
Time Series
1. Stationary Time Series (Ch 2 Enders)
2. Models with trend (Ch 4 Enders)
3. Vector Auto-Regressive models (Ch 5 Enders)
4. Cointegration and error correction models (Ch 6 Enders)
Cross-section and panel data
- Models with limited dependent variables
- Static and dynamic panel data models
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Computer Aided Lab | 11 |
Autonomous Student Learning | 120 |
Total | 155 |
This module will build on the material covered during the first semester econometric master's course. Students are strongly advised to have taken a course in Econometrics equivalent to the semester 1 Masters course, or its equivalent.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment: Time series projects. | Throughout the Trimester | n/a | Graded | No | 50 |
Group Project: Group data project | Varies over the Trimester | n/a | Graded | No | 20 |
Assignment: Panel data project | Varies over the Trimester | n/a | Graded | No | 30 |
Resit In | Terminal Exam |
---|---|
Summer | No |
• Group/class feedback, post-assessment
• Online automated feedback
• 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. Appointments will be given to those students wishing to get individual feedback on the empirical assignments and the final examination.
Name | Role |
---|---|
Dr Tiziana Brancaccio | Lecturer / Co-Lecturer |
Emanuele Albarosa | Tutor |