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Basic Econometrics
Brief description
This is a 20 credit course which consists of three parts.The first part introduces regression analysis: the basic idea behind the classical linear regression model (CLRM), the underlying assumptions, and the problem of estimation. Building on the two-variable model, we analyze a few extensions, the multiple regression model, and the matrix approach to the linear regression model. During the second part of the course, we consider hypothesis testing, and interval estimation, using both two-variable and multivariate regression models. The last part of the course analyzes the consequences on the estimators from relaxing the assumptions of the classical linear regression model, and discusses various remedies. We examine the cases of heteroskedasticity, autocorrelation, multicollinearity, nonlinearity, and non-stationarity.
Aims and intended learning outcomes
The aim of this course is to provide students with a basic foundation in econometric analysis combining theoretical knowledge with practical problems.
By the end of the course, students should be able to:
· Explain the principles that underlie the traditional methodology in econometrics and the CLRM.
· Illustrate the mathematical derivation of the Ordinary Least Squares (OLS) estimators, the standard error of the regression, and the variance of estimators.
· Assess the predictive power of the CLRM using various measures of fit.
· Explain the standard CLRM assumptions, and use them to show that the OLS estimators are best linear unbiased estimators.
· Construct hypothesis tests and compute t-statistics and confidence intervals, to conclude whether or not there are statistically significant relationships among the variables in the model.
· Detect the violation of the standard CLRM assumptions, evaluate the effects of the breakdown on the OLS estimators and their variance, and apply some remedial procedures.
· Apply the OLS method with appropriate computer software to various types of datasets.
Timetable
The class will meet for a total of 28 hours during the first semester, divided into ten 2-hour lectures and four 2-hour labs.
Weeks Lecture Tutorial / Lab
Week 1 Lecture 1
Week 2 Lecture 2
Week 3 Lecture 3
Week 4 Lecture 4
Week 5 Lecture 5 Lab 1
Week 6 Lecture 6
Week 7 Lecture 7 Lab 2
Week 8 Lecture 8 Lab 3
Week 9 Lecture 9
Week 10 Lecture 10 Lab 4
Texts/Additional reading
Course main text
Gujarati, D.N. (2003). Basic Econometrics, 4th edition, London: McGraw-Hill (DG)
Other texts
Chiang, A. (1974). Fundamental Methods of Mathematical Economics, Tokyo; London: McGraw-Hill Kogakusha.
Greene, W.H. (2002). Econometric Analysis, 5th edition, Prentice Hall.
Gujarati, D.N. (1992). Essentials of Econometrics, 2nd edition, New York; London: McGraw-Hill.
Johnston, J. (1984). Econometric Methods, 3rd edition, New York; London: McGraw-Hill.
Kennedy, P. (1998). A Guide to Econometrics, 4th edition, Oxford: Blackwell.
Maddala, G.S. (2001). Introduction to Econometrics, 3rd edition, Chichester; New York: John Wiley.
Stock, J. and Watson, M. (2003). Introduction to Econometrics, Boston, Mass.:
London: Addison Wesley.