ECON42000 Econometrics

Academic Year 2020/2021

This is a post-graduate (Masters) level course in econometrics. We will cover estimaton and testing of the general linear regression model, including departures from the classical conditions of exogeneous regressors and spherical errors. We then consider the method of maximum likelihood with some of its applications.

Show/hide contentOpenClose All

Curricular information is subject to change

Learning Outcomes:

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 Hours: 
Student Effort Type Hours
Lectures

30
Computer Aided Lab

30
Autonomous Student Learning

110
Total

170
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:

Students taking this course should have a good foundation in statistics including basic probability theory and statistical inference. Knowledge of linear regression is desirable. In addition, students should be familiar with multivariate calculus and matrix algebra.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Examination: Final exam 2 hour End of Trimester Exam No Graded No

70
Assignment: Students will be assigned data which they should analyse & then write-up the results. They may, if they choose, work in groups of up to three (3) people. Week 11 n/a Graded No

15
Assignment: Students will be assigned data which they should analyse & then write-up the results. They may, if they choose, work in groups of up to three (3) people. Week 6 n/a Graded No

15

Carry forward of passed components
No
 
Resit In Terminal Exam
Spring Yes - 2 Hour
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 computer based tests, midterm test and the final examination.

Verbeek, A Guide to Modern Econometrics, Wiley.