Module overview
Building on the econometric content learned in the second year this module introduces students to advanced topics in econometrics. The module will familiarise students with state of the art methods of model selection in econometrics, giving them access to the fundamental methods in machine learning relevant for statistical inference in economic contexts. It will also introduce students to frontier methods in causal inference to enable meaningful policy evaluation. Applications to economic problem will be used throughout to illustrate the methods.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- use programming techniques from machine learning for data analysis
- apply economic logical analysis to econometric models of machine learning for the identification of causal inference
- use data, including from large datasets, for statistical inference on the quantitative or qualitative workings of economic mechanisms and policies.
- use model selection methods to organise and analyse economic data in an informative manner.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- implementing machine learning techniques using adequate computational tools.
- methods of machine learning and model selection.
- fundamental machine learning methods for quantitative economic and econometric analysis.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- use quantitative reasoning and analyse and interpret data using machine learning
- collaborate with others and recognise problems of and possible solutions for structuring team work in a data analysis project
Syllabus
After a brief review of the relevant fundamental econometric methodology the module will cover in depth various methods of model selection such as LASSO and random forests and give a thorough introduction to algorithmic model selection. The module will also cover key topics in causal inference. The methods will be applied to address economic policy questions, retrieving and manipulating large datasets from sources as necessary.
Learning and Teaching
Teaching and learning methods
Lectures and (computer-based) Masterclasses
| Type | Hours |
|---|---|
| Tutorial | 8 |
| Independent Study | 120 |
| Lecture | 20 |
| Workshops | 2 |
| Total study time | 150 |
Resources & Reading list
Internet Resources
Assessment
Assessment strategy
Continuous assessment through three take-home data analysis assignments, supported by continuous formative assessment through data analysis exercises. There is no final exam. This is the same for an internal repeat. Assessment for external repeat and referral is through a single piece of coursework.
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 10% |
| Coursework assignment(s) | 70% |
| Coursework assignment(s) | 15% |
| Online test | 5% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 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 |
|---|---|
| Coursework assignment(s) | 100% |
Repeat Information
Repeat type: Internal & External