Module Information

Module Identifier
PGM0910
Module Title
Advanced Quantitative Data Analysis Techniques
Academic Year
2015/2016
Co-ordinator
Semester
Semester 2 (Taught over 2 semesters)
External Examiners
  • Dr Jane Wellens (Head of Graduate School - University of Nottingham)
 
Other Staff

Course Delivery

Delivery Type Delivery length / details
Seminar 11 x 1 Hour Seminars
 

Assessment

Assessment Type Assessment length / details Proportion
Semester Assessment 2,500 word assessment  explaining the principles and core methods of high frequency data analysis  100%
Supplementary Assessment 2,500 word essay  - resubmitting essay using a different set of data from original submission  100%

Learning Outcomes

On successful completion of this module students should be able to:

Demonstrate competency in understanding and applying a range of research methods, to enable the collection of quantitative data
Display knowledge of the management of research data, and the use of IT in such data management
Demonstrate skills in conducting research in a manner which is consistent with professional practice
Demonstrate an awareness of the key skills involved in analysing quantitative data
Demonstrate an awareness of a range of quantitative techniques and related software
Demonstrate an appreciation of relevant applications of quantitative methodologies

Brief description

This module was designed to be an integral component of the RT courses which the University has introduced in order to meet the joint funding Research councils statement on Research Training. Through this module Masters and PhD students will gain a broad knowledge of a range of transferable skills which they can apply in a variety of research contexts.

Aims

The module is aimed at students who have previously studied basic quantitative techniques such as regression analysis. It complements the coverage of quantitaive methods offered in PGM0620 (Empirical Methods).

Content

Event Study Methodology
High Frequency data analysis
Efficiency frontier models
Limited dependent varianble models
Performance measurement
Vector Autoregression (VAR) Analysis and the Johansen ML procedure
Autoregressive Distributed Lag models - Cointegration

Notes

This module is at CQFW Level 7