ECON41820 Econometrics

Academic Year 2023/2024

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
Lectures

30

Computer Aided Lab

20

Autonomous Student Learning

100

Total

150

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 Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Class Test: Computer lab test: students will be given a dataset and asked to perform empirical analysis. Unspecified n/a Graded No

30

No
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

20

No
Examination: Midterm Exam Week 7 No Graded No

20

No
Examination: Final exam 2 hour End of Trimester Exam No Graded 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 in detail during tutorials 2. General feedback will be provided to the class.

Verbeek, A Guide to Modern Econometrics, Wiley
Wooldridge J., Introductory Econometrics - A modern approach