– Introduction to Econometrics
What is econometrics? Why a separate discipline?, methodology of econometrics, types of econometrics, understanding econometric modeling, use of econometrics for social scientists and business executives, regression as main tool of econometrics.
– Two Variable Regression Model
Population regression function, the significance of the stochastic disturbance term, the sample regression function, the role of statistical packages for econometric study.
– Estimation Two Variable Regression Model
The method of ordinary least squares (OLS), the basic assumption underlying the method of OLS, the properties of least squares estimators: Gauss Markov Theorem,
Standard error of the estimates, estimator of the variance of the disturbance term, Coefficient of determination, some illustrative examples of the two variable regression models.
– Classical Normal Linear Regression Model (CNLRM)
Normality assumption of the disturbance term, why the normality assumption? properties of OLS estimators under normality assumption, the method of maximum likelihood (ML),
– Two Variable Regression: Interval Estimation and Hypothesis Testing
The chi-square, t distribution and F distribution, hypothesis testing, confidence interval approach & test of significance approach.
– Extensions of the Two Variable Linear Regression Model:
Regression through the origin, scaling and units of measurement, different functional forms of regression models (double log model, semi-log, reciprocal model and the logarithmic reciprocal model), interpretation of estimated parameters, measurement of elasticity.
– Estimation of Multiple Regression Model
The matrix approach to linear regression model, assumptions of classical linear regression model in matrix notation, OLS estimation, variance covariance matrix of the estimators, R. square and adjusted R square, hypothesis testing of equality of two regression coefficients, restricted least squares, testing for structural or parameter stability of regression models: the Chow test, testing the functional form of regression : choosing between linear and log-linear regression models, forecasting with multiple regression.
– Demonstration of Statistical Packages for Social Sciences (SPSS) / Econometric Views/STATA
– Introduction to Econometrics
1. Griffiths, William E., R. Carter Hill and George G. Judge: Learning and Practicing Econometrics, John Wiley & Sons, New York, 1993.
2. Gujarati, D.N.:Basic Econometrics,4th Edition, McGraw Hill Publications,2003.
3. Pindyck, R.S., and D.L. Rubinfeld.: Econometric Models and Econometric Forecasts4th Edition, Irvin McGraw Hill, 1998.
4. Johnston, J.: Econometric Methods, 3rd edition, McGraw Hill, New York, 1984.
5. Kmenta, Jan: Elements of Econometrics, 2nd edition, Macmillan, New York, 1986.
6. Koutsoyiannis. A.: Introductory Econometrics, Harper & Row, New York, 1973.
7. Maddlala, G.S.: Introduction to Econometrics, John Wiley & Sons, New York, 1970.
8. Andrew M. Jones, Health Econometrics, University of York.