|| PGM0620 |
|| EMPIRICAL METHODS |
|| 2006/2007 |
|| Dr Mark J Rhodes |
|| Semester 2 (Taught over 2 semesters) |
| Course delivery
|| Lecture || 22 Hours. |
|| Practical || 16 Hours. |
|| Seminars / Tutorials || 4 hours |
|Assessment Type||Assessment Length/Details||Proportion|
|Semester Exam||2 Hours EXAM Semester two. Questions will be drawn from all of the module content.||50%|
|Semester Assessment|| ASSESSED PRACTICAL COURSEWORK Semester one. Each student will be allocated a data set and required to undertake an analysis and to interpret the results with particular reference to learning outcomes 1-3.||20%|
|Semester Assessment|| ASSESSED PROJECT WORK Semester two. Students will be required to undertake a brief research project, collecting their own data and identifying appropriate analytical methods as identified principally from learning outcomes 4-7.||30%|
On completion of this module the students should be able to critically evaluate:
1) The use of classical linear regression in estimation and inference.
2) The consequences of the failure of the classical regression assumptions, their diagnosis, consequences and solutions.
3) The specification, estimation and properties of dynamic time series models, including those with dynamic error structures.
4) The specification, estimation and properties of simultaneous equation regression models.
5) The implications of unit roots in time series and the importance of cointegration.
6) The use of single and multiple equation estimation techniques and
7) The advantages and shortcomings of panal data and the techniques for estimation of panel data models and their application.
The module is an advanced level study of econometrics. Building from basic principles in statistics we will discuss practical applications, both in the interpretation of empirical results and the use of computers to estimate original models.
Course Outline - Semester 1
1. Introduction to CAPM and OLS
2. Regression analysis
3. Features of OLS and interpretation of results
4. Multicollinearity and Micronumerosity
6. Dynamic Models
8. Functional form and Normality
Course Outline - Semester 2
1. Univariate Time Series Modelling
2. Multivariate modelling: Estimation of Simultaneous Equations Models
3. Modelling Long-run Financial Relationships: Unit Roots and Cointegration
4. Modelling Volatility in Financial Time Series
5. Models that combine cross-section and time-series data
The module is an advanced level study of econometrics. We will examine the uses of econometrics in the study of financial institutions and markets. Building from basic principles in finance and statistics we will discuss the practical application, both in the interpretation of empirical results in finance and the use of computers to estimate original models.
This module is at CQFW Level 7