Module Information
Course Delivery
| Delivery Type | Delivery length / details |
|---|---|
| Other | 10 x 1 hour computer class in sem1. 12 x 1 hour computer class in sem 2 |
| Lecture | 12 x 1 hour |
Assessment
| Assessment Type | Assessment length / details | Proportion |
|---|---|---|
| Semester Assessment | Project in Semester 2 | 30% |
| Semester Assessment | Assessed practical in semester 1. | 20% |
| Semester Exam | 2 Hours EXAM | 50% |
Learning Outcomes
On successful completion of this module students should be able to:
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.
Brief description
The module will cover conceptual and practical material enabling students to both critically evaluate the empirical research of other authors and to conduct their own analyses. From essential foundations discussed in semester 1 the module progresses to an advanced level study of econometrics.
Aims
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.
Content
Introduction to CAPM and OLS
Regression Analysis
Features of OLS and interpretation of results
Multicollinearity and Micronumerosity
Autocorrelation
Dynamic Models
Heteroscedasticity
Functional form and normality
Semester 2
Univariate Time Series Modelling
Multivariate Modelling: Estimation of Simultaeeous Equations Models
Modelling long-run Financial Relationships: Unit Roots and Cointegration
Modelling Volatility in Financial Time Series
Models that combine cross-section and time-series data.
Notes
This module is at CQFW Level 7