ECONOMETRIC METHODS
FIN5EME
2017
Credit points: 15
Subject outline
In this subject you will cover the modern regression and time series methods applicable to business and financial data. The main topics include: Application of the regression methods to corporate and business decisions; Estimation of asset pricing models and its applications; Efficient market hypothesis and financial asset predictability; Modelling long-run relationships in business and economics; and Modelling financial volatility and its application to risk management. Strong emphasis will be given to application of the methods using the econometrics package Eviews.
School: La Trobe Business School
Credit points: 15
Subject Co-ordinator: Petko Kalev
Available to Study Abroad Students: Yes
Subject year level: Year Level 5 - Masters
Exchange Students: Yes
Subject particulars
Subject rules
Prerequisites: ECO5SBF or FIN5SBF or BUS5SBF
Co-requisites: N/A
Incompatible subjects: ECO2EME
Equivalent subjects: N/A
Special conditions: N/A
Graduate capabilities & intended learning outcomes
01. Understand simple regression
- Activities:
- lecture, tutorial, and assignment
- Related graduate capabilities and elements:
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
02. Understand the properties of good estimators, the Gauss-Markov Theorem and the assumptions it requires.
- Activities:
- lecture, tutorial, and assignment
- Related graduate capabilities and elements:
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
03. Critically analyse an estimated regression model
- Activities:
- lecture, tutorial, and assignment
- Related graduate capabilities and elements:
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
04. Develop reasonable estimation procedures for multiple linear regression in the presence of heteroskedasticity and autocorrelation
- Activities:
- lecture, tutorial, and assignment
- Related graduate capabilities and elements:
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
05. Use the software package EViews, and to diagnose violation of assumptions, and perform estimation and hypothesis tests
- Activities:
- lecture, tutorial, and assignment
- Related graduate capabilities and elements:
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
06. Apply the econometric methods to solve real-world problems
- Activities:
- lecture, tutorial, and assignment
- Related graduate capabilities and elements:
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Inquiry and Analytical Skills(Critical Thinking,Creative Problem-solving)
- Discipline -Specific Knowledge and Skills(Discipline-Specific Knowledge and Skills)
Melbourne, 2017, Semester 1, Day
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Enrolment information:
Subject Instance Co-ordinator: Petko Kalev
Class requirements
Computer LaboratoryWeek: 11 - 22
One 1.0 hours computer laboratory per week on weekdays during the day from week 11 to week 22 and delivered via face-to-face.
LectureWeek: 10 - 22
One 2.0 hours lecture per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
TutorialWeek: 10 - 22
One 1.0 hours tutorial per week on weekdays during the day from week 10 to week 22 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| one 3-hour final examination | 60 | 01, 02, 03, 04, 05, 06 | |
| 2 assignments (20% each, each with 1500 words) | 40 | 01, 02, 03, 04, 05, 06 |
Melbourne, 2017, Semester 2, Day
Overview
Online enrolment: Yes
Maximum enrolment size: N/A
Enrolment information:
Subject Instance Co-ordinator: Petko Kalev
Class requirements
Computer LaboratoryWeek: 32 - 43
One 1.0 hours computer laboratory per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.
LectureWeek: 31 - 43
One 2.0 hours lecture per week on weekdays during the day from week 31 to week 43 and delivered via face-to-face.
TutorialWeek: 32 - 43
One 1.0 hours tutorial per week on weekdays during the day from week 32 to week 43 and delivered via face-to-face.
Assessments
| Assessment element | Comments | % | ILO* |
|---|---|---|---|
| one 3-hour final examination | 60 | 01, 02, 03, 04, 05, 06 | |
| 2 assignments (20% each, each with 1500 words) | 40 | 01, 02, 03, 04, 05, 06 |