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ECON42770

Academic Year 2026/2027

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

The United Nations identified seventeen Sustainable Development Goals (SDGs) as core to the 2030 Agenda for Sustainable Development, and UCD contributes in general to SDG 4 Quality Education. Further SDGs explored within this module if relevant are listed below. A scale of 1 - 5 indicates the extent to which the SDG is covered.


 

Student Effort Hours:
Student Effort Type Hours
Lectures

33
Computer Aided Lab

22
Specified Learning Activities

100
Autonomous Student Learning

100
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
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
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 12 Graded No
20
 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
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) - Autumn: Weeks 2-12 Fri 10:00 - 10:50
Autumn Lecture Offering 1 Week(s) - Autumn: Weeks 2-12 Tues 12:00 - 13:50
Autumn Computer Aided Lab Offering 1 Week(s) - Autumn: Weeks 2-12 Fri 15:00 - 16:50
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