ECON41820 Econometrics

Academic Year 2022/2023

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.

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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
Autonomous Student Learning

175

Lectures

30

Computer Aided Lab

20

Total

225

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

Not applicable to this module.


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: 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

25

Examination: Midterm Exam Week 7 No Graded No

25

Examination: Final exam 2 hour End of Trimester Exam No Graded No

50


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

• Group/class feedback, post-assessment
• 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. General feedback will be provided to the class.

Verbeek, A Guide to Modern Econometrics, Wiley
Wooldridge J., Econometric Analysis of Cross Section and Panel Data

For students who have not studied Econometrics before, a useful resource that may help bridge the gap is
Wooldridge J., Introductory Econometrics - A modern approach
Name Role
Manuel Rodríguez Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Autumn
     
Lecture Offering 1 Week(s) - 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 Tues 10:00 - 12:50
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
Autumn