Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Introductory Econemetrics for Finance ECON5131

  • Academic Session: 2022-23
  • School: Adam Smith Business School
  • Credits: 10
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 2
  • Available to Visiting Students: No
  • Available to Erasmus Students: No

Short Description

This 6-week-teaching course aims to quickly foster abilities to apply basic econometric tools to finance-related data. Throughout the course, empirical applications are emphasised.

Timetable

The course runs over six weeks. Within each week:

- One 2-hour teaching class each week.

- One 1-hour laboratory session each week.

Requirements of Entry

Please refer to the current postgraduate prospectus at: http://www.gla.ac.uk/postgraduate/ 

Excluded Courses

None

Co-requisites

None

Assessment

1-hour In-Course Exam at the end of Semester 2.

2-hour Degree Exam in April/May.

Main Assessment In: April/May

Course Aims

The purpose of this course is to provide students with basic insights into econometrics in order to understand and

critically analyse financial decisions. The course provides students with a range of statistical tools to analyse financial

data. The course thereby aims to provide students with a combination of critical appraisal of the importance of

statistical insights to inform financial decision, and to equip students to carry out basic statistical analyses of financial

data sets.

Intended Learning Outcomes of Course

By the end of this course students will be able to:

 

1. Critically analyse statistical data to inform financial decision-making;

2. Interpret key statistical concepts and apply them to the analysis of relevant numerical data;

3. Apply basic statistical techniques and models to the analysis of numerical data (e.g. Classical Linear

Regression; Uni-Variate Time Series analysis; Panel Regressions; Limited Dependent Model).

4. Articulate and evaluate the importance of statistical insights to inform financial strategy development.

Minimum Requirement for Award of Credits

Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment.