The module will proceed from a review of known content (matrix algebra, linear regression, hypothesis testing) to more advanced topics such as multiple linear regression, heteroskedasticity, restrictions in hypothesis testing, issues of model misspecification, and an introduction to asymptotic theory and time series models to exploit large panel datasets for statistical inference. The module will thus equip students with fundamental methods for statistical inference on large panel datasets.
Prerequisites: ECON2006 or MATH2011
Aims and Objectives
Having successfully completed this module you will be able to:
- Demonstrate knowledge and understanding of statistical methods for analysing large datasets using traditional (classic regression) and algorithmic (numerical) methods.
- Demonstrate knowledge and understanding of analytical methods and the statistical tools for economic, specifically econometric analysis, in particular the assumptions and basic theory of classical linear regression how to deal with violations of standard assumptions of classical linear regression.
- Use quantitative reasoning in economic contexts 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
- Use data for statistical inference on the quantitative or qualitative workings of economic mechanisms and policies by setting up statistical tests.
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 (or equivalent) econometric package, and interpreting its results, you will learn to demonstrate knowledge of the appropriate econometric methods
Learning and Teaching
Teaching and learning methods
Lectures, masterclasses (tutorials), computer lab sessions.
|Total study time||150|
Resources & Reading list
Econometric Analysis. Prentice Hall International.
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani (2013 ). An introduction to Statistical Learning: With Application in R, . Springer Texts in Statistics:.
Introductory Econometrics: A Modern Approach. South-Western Cengage Learning.
Introduction to Econometrics. John Wiley & Sons.
Assessment in this module is through coursework in form problem sets (50% of the final mark) and an end of semester examination (50%). The coursework both forms your ideas, and assesses your ability to apply econometric methods. The examination measures your grasp of the theory, and of the detailed rationale for the econometric methods.
This is how we’ll formally assess what you have learned in this module.
This is how we’ll assess you if you don’t meet the criteria to pass this module.
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.
Repeat type: Internal & External