Module overview
The module will proceed from a review of known content (like matrix algebra, linear regression, hypothesis testing) to more advanced topics such as multiple linear regression, heteroscedasticity, restrictions in hypothesis testing, issues of model misspecification, and an introduction to big data techniques such as shrinkage methods to exploit large datasets for statistical inference. The module will thus equip students with fundamental methods for statistical inference on large datasets.
Linked modules
Prerequisites: (ECON1008 and ECON2043 ) or (ECON2041or MATH2011)
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- traditional statistical methods (classical regression) for analysing large datasets
- the analytical methods and tools for the econometric analysis of economic data, including of Big Data, and their theoretical underpinnings.
- computational and algorithmic (numerical) methods for analysing large datasets.
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- program adequate software to implement algorithmic (numerical) statistical methods relevant for econometric data analysis
- describe the theoretical assumptions required for the validity of classical linear regression and propose adequate solutions if these assumptions are violated.
- use data for statistical inference on the quantitative or qualitative workings of economic mechanisms and policies by setting up statistical tests
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- use quantitative reasoning and analyse and interpret data using adequate computer software, by performing basic regression programming in an econometric package and be able to evaluate regression estimates
Syllabus
The module explains the underlying statistical concepts, and establishes some of the formal results to understand the theoretical basis for regression. By using the STATA, R or equivalent econometric software packages, and interpreting its results, you will learn to demonstrate knowledge of the appropriate econometric methods.
Learning and Teaching
Teaching and learning methods
Lectures, masterclasses, computer lab sessions.
| Type | Hours |
|---|---|
| Workshops | 2 |
| Tutorial | 9 |
| Independent Study | 119 |
| Lecture | 20 |
| Total study time | 150 |
Resources & Reading list
Textbooks
G. James, D. Witten, T. Hastie, R. Tibshirani (2013). An introduction to Statistical Learning: With Application in R. Springer.
J. Wooldridge (2018). Introductory Econometrics: A Modern Approch. South-Western Cengage Learning.
Assessment
Assessment strategy
One coursework assignment, one online test, quiz during the semester and a final exam, supported by formative assessment in form of problem sets and data analysis exercises. This is the same for internal repeats. Referral and external repeat assessment are through 100% final exam.
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Online test | 10% |
| Blackboard quizzes | 10% |
| Final Exam | 60% |
| Coursework assignment(s) | 20% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Final Exam | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Final Exam | 100% |
Repeat Information
Repeat type: Internal & External