Module overview
The module will familiarise students with the parts of statistical distribution theory and statistical inference that are essential to a full understanding of econometrics and applied statistics. It will give student a thorough introduction to the theoretical concepts underlying modern Econometrics. It develops ideas presented in ECON1007 and ECON1011 and applies mathematical techniques from ECON1008.
Linked modules
Pre: Rec - ECON1011
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- key concepts and methods from statistical theory relevant for economic data analysis.
- properties of statistical models relevant for the anaylsis of small and large datasets and their distributional properties;
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- abstract the essential features of probabilistic models and specify statistical procedures to assess their properties through estimation.
- use statistical inference to gain insight from data on the effects of events and the workings of the economy;
- identify violations of the theoretical assumptions required for statistical inference and describe their consequences and possible remedies.
- apply logical analysis to statistical models and make use of inductive reasoning.
- analyse, interpret and present economic data in an informative manner.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- analyse data using adequate techniques.
Syllabus
The module covers the following topics:
- Distribution Theory: Multivariate distributions: marginal and conditional distributions, multivariate normal. Relations between normal, chi-square, F, t and Cauchy distributions.
- Asymptotic theory: probability limits and the central limit theorem.
- Inference: Estimation: Cramer-Rao inequality and Rao-Blackwell theorem. Maximum likelihood. Hypothesis testing: Neyman-Pearson Lemma and Likelihood Ratio Tests.
The module uses mathematical techniques (mainly integration, differentiation and limits) to establish relationships between distributions, and the principles of classical statistical inference. A variety of distributions (Binomial, Poissson, negative Binomial, exponential, normal, gamma) can be used to exemplify and illustrate both the distribution theory and the inference.
Learning and Teaching
Teaching and learning methods
Lectures and tutorials.
| Type | Hours |
|---|---|
| Tutorial | 8 |
| Independent Study | 122 |
| Lecture | 20 |
| Total study time | 150 |
Resources & Reading list
Textbooks
Wackerly, D.D. Mendenhall, W. and Scheaffer, R.L (2007). Mathematical Statistics with Applications.
Assessment
Assessment strategy
Two problem sets during the semester and a final exam, supported by formative assessment through problem sets. This is the same for an internal repeat. Assessment for external repeat or referral is through final exam only.
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Coursework assignment(s) | 5% |
| Final Exam | 90% |
| Coursework assignment(s) | 5% |
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