Basic Econometrics
Year: 2009-2010
Course credits: 20
Course code: 0SRS
Semester: 1
Course co-ordinator and lecturer: Dr Alexandros Kontonikas
Prerequisites: None
Course description
This four-part course provides students with a basic foundation in econometrics combining theoretical underpinnings with practical problems. The first part is a review of the basic statistical concepts and matrix algebra. The second 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, it analyses a few extensions, the multiple regression model, and the matrix approach to the linear regression model. The third part of the course reviews hypothesis testing and interval estimation, both on the 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. It examines the cases of heteroskedasticity, autocorrelation, multicollinearity, non-linearity and non-stationarity.
Learning and teaching methods
Ten 2-hour lectures, two 2-hour review lectures, and three 2-hour computer labs.
Course text
- The main text for this course is Gujarati, D.N. (2003). Basic Econometrics, 4th edition, London: McGraw-Hill.
Other recommended readings include:
- Maddala, G.S. (2001). Introduction to Econometrics, 3rd edition, Chichester: Wiley.
- Stock J. and Watson M. (2003). Introduction to Econometrics, London: Addison Wesley.
- Greene, W.H. (2002). Econometric Analysis, 5th edition, Prentice-Hall.
Assessment
- Coursework (25%) and examination (75%)
- Coursework: 1 quantitative / computer assignment
- Examination: two-hour written examination taken at the April/May examination diet