|Delivery Type||Delivery length / details|
|Seminar||8 x 1 Hour Seminars|
|Lecture||10 x 2 Hour Lectures|
|Lecture||5 x 1 Hour Lectures|
|Practical||5 x 2 Hour Practicals|
|Assessment Type||Assessment length / details||Proportion|
|Semester Exam||3 Hours (Unseen Book)||50%|
|Semester Assessment||Written Report - Empirical project (1500 words)||30%|
|Semester Assessment||Written Report - Practical Assessment (1500 words)||20%|
|Supplementary Exam||3 Hours (Unseen Book)||50%|
|Supplementary Assessment||Written Report - Empirical project (1500 words)||30%|
|Supplementary Assessment||Written Report - Practical Assessment (1500 words)||20%|
On successful completion of this module students should be able to:
1. Estimate and interpret classical linear regression models.
2. Explain the consequences of the failure of the classical regression assumptions, diagnose problems and identify solutions.
3. Specify and estimate appropriate dynamic models for univariate and multivariate time series.
4. Apply difference-in-difference and matching estimation methods.
5. Specify and estimate appropriate dynamic models for panel data sets.
This module provides advanced coverage of econometric methods and practices that are used to model financial data. Although the main emphasis is on practice rather than theory, a sufficient theoretical grounding is provided to develop a critical awareness of the strengths and weaknesses of the modelling techniques that are employed. There is emphasis on the design, estimation and evaluation of appropriate econometric models using econometrics software.
• Testing the assumptions of a regression model
• Stationarity and non-stationarity of financial time series
• Vector autoregressive models
• Cointegration between non-stationary time series
• Modelling volatility in financial time series
• Difference-in-difference estimators, and matching estimators
• Dynamic panel regression models
|Skills Type||Skills details|
|Application of Number||• Develop an easy familiarity with numerical data sources and numerical data • Apply numerical data to problem solving with care and accuracy • Assess the reasonableness of and interpret numerical solutions • Support assertions/arguments with appropriately developed and presented numerical data Apply complex mathematical formulae|
|Communication||• Develop confidence in and clarity of oral communication via example class/tutorial participation • Develop clarity and focus of written communication via development of answers to self-study questions Develop and use appropriate subject-specific vocabulary in oral and written communication|
|Improving own Learning and Performance||• Identify and distil the key issues covered by lectures, tutorials and self-study • Identify and use a range of learning resources • Investigate benefits of small group working on self-study Structure study to accommodate intensive learning|
|Information Technology||• Use a variety of electronic web- and library-based resources to review available information and retrieve pertinent information Use spreadsheet software to complete elements of the self-study (e.g., for ease of tabulated numerical calculations, production of summary statistics, production of graphs, etc.)|
|Personal Development and Career planning||• Preparation for seminar tasks will encourage initiative, independence and self-awareness • Identify a variety of potential career opportunities within the financial and professional services sector.|
|Problem solving||• Identify the precise problem to be solved • Assess which data are pertinent to the problem • Recognize that alternative solution methods might be available • Select and apply appropriate methods for solving the problem Assess the reasonableness of problem solutions and interpret those solutions|
|Research skills||• Identify which information sources are available to: o facilitate module study (understanding, wider reading) o provide data which allow application of module learning in a real world context Properly reference/attribute information sources|
|Subject Specific Skills||Develop competence in understanding and appropriately applying advanced level econometrics models to practice. • Develop competence in understanding the use of classical linear regression in estimation and inference • Develop competence in understanding both the advantages and shortcomings of panel data and the techniques for estimation of panel data models and their application.|
|Team work||Develop experience of team work and develop team working skills via small group working on self-study|
This module is at CQFW Level 7