ECON42230 Advanced Econometrics

Academic Year 2020/2021

During this course you will be introduced to econometric models and estimation methods widely used in applied economics. Both micro and macro econometrics models will be treated and topics will include among others: limited dependent variable and sample selection models, panel data and treatment effects, univariate time series (ARMA, trend stationarity and unit roots), multivariate time series (ARDL, VAR, cointegration, VECM).

The approach aims at strengthening your theoretical econometric background and deepening your understanding of the various applications of such models. It is also a hands-on module and you will be taught how to estimate the different models using STATA .

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Curricular information is subject to change

Learning Outcomes:

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 .

Indicative Module Content:

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 Hours: 
Student Effort Type Hours
Lectures

24
Computer Aided Lab

11
Autonomous Student Learning

120
Total

155
Approaches to Teaching and Learning:
The modules comprises lectures and hands-on computer lab sessions; the latter allow students to apply the techniques learned on real data and to develop confidence in handling datasets and statistical software. 
Requirements, Exclusions and Recommendations
Learning Recommendations:

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.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % 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

Carry forward of passed components
No
 
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Group/class feedback, post-assessment
• Online automated feedback
• Self-assessment activities

How will my Feedback be Delivered?

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.

Verbeek, A Guide to Modern Econometrics, Wiley.
Enders, Applied Econometrics Time Series, Wiley.
Name Role
Dr Tiziana Brancaccio Lecturer / Co-Lecturer
Emanuele Albarosa Tutor