Programme And Module Handbook
Course Details in 2025/26 Session

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Module Title Statistical Methods in Finance and Economics
Department Mathematics
Module Code 06 27099
Module Lead Dr Hui Li
Level Masters Level
Credits 20
Semester Semester 2
Pre-requisites LI Statistics - (06 25671) LH Statistics - (06 27147)
Restrictions None
Description This course is designed for students with limited or no prior finance or economic theory background. It emphasizes the understanding of quantitative methods, model evaluations, and the techniques for empirical studies in finance and economics.
This module starts with an introduction to general financial and economic concepts, then it will cover the basics and extension of ordinary least square methods, heteroscedasticity, autocorrelation, multicollinearity, model specifications, simultaneous equation models, binary and discrete choice models, qualitative and limited dependent variable models, time series analysis, panel data models, and nonparametric analysis with their applications in finance and Economics. Students will gain hands-on experience formulating and estimating models, interpreting results, and making forecasts.
Learning Outcomes By the end of the module students should be able to:
  • Demonstrate an understanding of the nature of statistical inferential procedures involved in analysing financial and economics data;
  • Formulate models to solve some empirical economic problems;
  • Apply appropriate statistical methods and techniques to understand relationships among variables;
  • Use statistical computing programme of SAS/SPSS;
  • Demonstrate an understanding of the power and limitations of applied statistical analysis;
  • Perform and present research by using relevant data and statistical tools;
  • Demonstrate a comprehensive knowledge beyond the taught syllabus from personal exploration of the subject.
Assessment 27099-01 : Raw Module Mark : Coursework (100%)
Assessment Methods & Exceptions 3 hour Written Unseen Examination (80%); In-course Assessment (20%).
Other None
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