13503 (v.3) Econometrics (Introductory) 511

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Area:	School of Economics and Finance
Credits:	25.0
Contact Hours:	4.0
Lecture:	1 x 2 Hours Weekly
Tutorial:	1 x 1 Hours Weekly
Laboratory:	1 x 1 Hours Weekly
Syllabus:	A review of elementary statistics, two-variable regression and multiple regression models, heteroskedasticity, autocorrelation, multicollinearity, dummy and truncated variables. It is an introduction to simultaneous equation models, errors in variables, model selection, diagnostic and specification testing.
Unit Outcomes:	On successful completion of this unit students should be able to: Explain the concept of ordinary least squares estimation and apply it to estimation, inference, and forecasting using economic problems: Use the EViews software programme to find least squares estimates for simple linear regression and multiple regression models: Interpret EViews output and place that interpretation in an economic context relevant to the model being estimated: Use EViews output to perform tests for a variety of hypotheses:Demonstrate an understanding of how to combine sample and nonsample information: Define Heteroskedasticity, explain the consequences of it for the least squares estimator and compute generalised least squares estimates for alternative assumptions about the error variance: define autocorrelation in the form of an AR(1) error, test for autocorrelation using the Durbin -Watson test and use EViews to obtain generalized least squares estimates in the presence of an AR (1) error: Explain what is meant by collinear economic variables, and know the consequences of exact collinearity and high but not exact economic collinearity and how to over come it.
Text and references listed above are for your information only and current as of September 30, 2003. Please check with the unit coordinator for up-to-date information.
Unit References:	Brown, W.s. (1991), Introductory Econometrics, West., Doran, H.E. (1989), Applied Regression Analysis in Econometrics, Marcel Dekker. Dougherty, C. (1992), Introduction to Econometrics, Oxford, Griffiths, W.E., Hill, R.C. and Judge G.G. (1993), Learning and Practicing Econometrics, John Wiley and Sons. Gujarati, D.N. (1998), Basic Econometrics, 2nd ed., McGraw-Hill. Gujarati, D.N. (1992), Essentials of Econometrics, McGraw-Hill. Johnson, A.C., Johnson, M.B., and Buse, R.C. (1987), Econometrics: Basic andapplied, Macmillan. Kennedy, P. (1998), A Guide to Econometrics, 4th ed., Basil Blackwell. Koutsoyiannis, A. (1977), Theory of Econometrics, 3rd ed., Macmillan. Maddala, G.S. (1992), Introduction to Econometrics, 2nd ed, Maxwell Macmillan. Pindyck, R.S. and Rubinfeld, D.L. (1998), Econometrics Modles and Economics Forecasts, 4th ed.,McGraw-Hill.
Unit Texts:	Hill, R.C., Griffiths, W.E.and Judge, G.G.(2001), Undergraduate Econometrics, 2nd ed, John Wiley and Sons. Reiman M.A. and Hill R.C. (2001), Using EViews for Undergraduate Econometrics, John Wiley and Sons.
Unit Assessment Breakdown:	Assignments 35%, Computing lab test 5%, Final Examination 60%. This is by grade/mark assessment.
Year Location Period Internal Area External Central External 2004 Bentley Campus Semester 1 Y   Y 2004 Bentley Campus Semester 2 Y     2004 Bentley Campus Summer Period Y