Financial Econometrics ACCFIN5217

  • Academic Session: 2024-25
  • School: Adam Smith Business School
  • Credits: 20
  • Level: Level 5 (SCQF level 11)
  • Typically Offered: Semester 1
  • Available to Visiting Students: No
  • Collaborative Online International Learning: No

Short Description

This course covers the concepts, theories and techniques in statistics and econometrics modelling relevant to conducting research in finance, economics and accounting.

Timetable

10 x 2 hour lectures and 6 x 1 hour tutorials. Asynchronous learning will include pre-recorded videos, reading lists, and problem solving activities for each unit.

Requirements of Entry

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

Excluded Courses

ACCFIN5039 Principles of Financial Econometrics

Co-requisites

None

Assessment

ILO

Assessment

Weighting

Word Length/Duration

1-6

Degree examination

75%

2 hours

Main Assessment In: December

Course Aims

The aim of this course is to enable students to develop, at an advanced level, an understanding of data and statistical analysis. The course will include coverage of the statistical and quantitative techniques, which are used in the study and practice of accounting and finance research.

Intended Learning Outcomes of Course

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

1. Appropriately apply probability and statistics theories to compare and illustrate probability distributions effectively with descriptive and inferential statistics to explain events relevant to research in finance, economics and accounting 

2. Apply in-depth techniques including mathematical derivations and statistical insights to select and use appropriate quantitative methods for problem solving and research in a hypothesis testing framework and formulate a rigorous hypothesis test using sample information

3. Evaluate advanced model specifications and the nature of data to conduct regression analysis to estimate the relationships between variables and evaluate the application of models to various data

4. Apply in-depth techniques including mathematical derivations and statistical insights to discuss and evaluate the violations of the ordinary least square estimation method and select appropriate estimation methods to deal with the consequence of the violations

5. Implement advanced methods with analytical and quantitative model specifications to analyse data of different nature

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.