Associate Professor Merran Evans and Dr David Harris
6 points
* Two 1-hour lectures and one 2-hour tutorial
per week
* First and second semester
* Clayton
* Prerequisite:
ETC1031 or equivalent
* Prohibitions: ETC2410, ETC3440
Objectives On successful completion of this subject students should have a rigorous understanding of the foundations of the classical regression model and be capable of deriving the properties of least squares estimators under classical assumptions and demonstrating the consequences of common violations of these assumptions; applying tests and appropriate estimation procedures about the relationships between variables and for violations of the underlying assumptions; applying this knowledge to estimate, analyse and forecast multiple regression models with typical economic and business data.
Synopsis An introduction to linear multiple regression methods; properties of least squares estimators; probability distributions and their applications to hypothesis testing; an introduction to the generalised least squares estimator; the problems of serial correlation, heteroscedasticity and multicollinearity.
Assessment Test (30 mins): 10%
* Two 2-hour
open-book tests: 30%
* Examination (2 hours): 60%
Recommended texts
Greene W H Econometric analysis 2nd edn, Macmillan,
1993
Judge, G G and others Introduction to the theory and practice of
econometrics 2nd edn, Wiley, 1988
Maddala G S Introduction to econometrics 2nd edn, Macmillan, 1992
Published by Monash University, Australia
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Approved by L Macdonald, Faculty of Business and Economics
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