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ECON42770

Academic Year 2025/2026

Econometrics (Level 4) (ECON42770)

Subject:
Economics
College:
Social Sciences & Law
School:
Economics
Level:
4 (Masters)
Credits:
10
Module Coordinator:
Dr Tiziana Brancaccio
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

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.

About this Module

Learning Outcomes:

Understanding and using econometric techniques at a masters levels.

Indicative Module Content:

1. Linear Regression
- model, OLS estimator
- Gauss-Markov assumptions, small sample properties, hypothesis testing
- asymptotic properties

2. More on the Linear Model
- missing data, outliers
- multicollinearity
- selecting regressors
- selecting functional form

3. Heteroskedasticity

4. Autocorrelation

5. Endogeneity
- Instrumental Variables estimator
- 2-Stage-Least-Squares and Generalized IV estimator
- Generalized Method of Moments

6. Maximum Likelihood
- 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
Specified Learning Activities

100
Autonomous Student Learning

100
Lectures

33
Computer Aided Lab

22
Total

255

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 Requirements:

Students must have a sound knowledge of matrix algebra and basic statistical concepts (random variables, expectation, common probability distribution - normal, chi square, t and F, joint distributions, point estimation and inference, interval estimation).


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Computer Lab Test Week 7 Alternative linear conversion grade scale 40% No
10
 No
Exam (In-person): Theory Midterm Week 7 Alternative linear conversion grade scale 40% No
15
 No
Group Work 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 Graded No
20
 No
Exam (In-person): Computer Lab Test Week 12 Alternative linear conversion grade scale 40% No
25
 No
Exam (In-person): Final Examination End of trimester
Duration:
2 hr(s)
 Alternative linear conversion grade scale 40% No
30
 No

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
• 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 during lectures/tutorials 2. General feedback will be provided to the class.

Name Role
Ms Manvi Jindal Tutor
Mr Ciaran Murphy 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 Tues 12:00 - 13:50
Autumn Lecture Offering 1 Week(s) - Autumn: Weeks 2-12 Tues 12:00 - 13:50
Autumn Lecture Offering 1 Week(s) - Autumn: Weeks 2-12 Wed 14:00 - 14:50
Autumn Computer Aided Lab Offering 1 Week(s) - Autumn: Weeks 2-12 Fri 16:00 - 17:50
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