fin5eme econometric methods
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.
SchoolLa Trobe Business School
Credit points15
Subject Co-ordinatorPetko Kalev
Available to Study Abroad StudentsYes
Subject year levelYear Level 5 - Masters
Exchange StudentsYes
Subject particulars
Subject rules
Prerequisites ECO5SBF or FIN5SBF or BUS5SBF
Co-requisitesN/A
Incompatible subjects ECO2EME
Equivalent subjectsN/A
Special conditionsN/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)
Subject options
Select to view your study options…
Melbourne, 2017, Semester 1, Day
Overview
Online enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorPetko 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 enrolmentYes
Maximum enrolment sizeN/A
Enrolment information
Subject Instance Co-ordinatorPetko 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 |