6 points · Two 1-hour lectures and one 2-hour tutorial per week · First and second semester · Clayton · Prerequisites: ETC1031 or equivalent · Prohibitions: ETC2400, ETC3440
Objectives On completion of this subject students should feel confident about undertaking straightforward empirical work with economic and accounting data, and have a preparation for more advanced study of the techniques of applied econometrics; understand the properties and limitations of classical multiple regression analysis; understand the role of tests which can be applied to an estimated regression model in order to determine its usefulness in explaining a relationship between variables; understand the role of procedures which can be followed to overcome limitations of the classical model.
Synopsis An introduction to linear multiple regression techniques; 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; the use of dummy variables; the choice of an appropriate functional form; study of the application of the techniques and tests through the study of published empirical work and the data sets used in them.
Assessment Class test: 10% · Written (1 assignment [computing and report]): 30% · Examination (2 hours): 60%
Prescribed texts
Gujarati D N Basic econometrics 3rd edn, McGraw-Hill, 1995
Back to the 1999 Business and Economics Handbook