Course Contents

1. Introduction to Econometrics 2. The Nature of Regression Analysis 3. Single Equation Regression Model 4. Two Variable Regression Models: The Problem of Estimation, Basic ideas, assumptions, Gauss Markov theorem, the coefficient of determination 5. Classical Normal Linear Regression Model 6. Two Variable Regression Models: The Problem of Inference: Hypothesis testing 7. Extension of Two-Variable Linear Regression Model: Different form of regression model 8. Multiple regression models: OLS estimation of the partial regression coefficients, the multiple coefficient of determination (R2) and adjusted R2, partial correlation coefficients, hypothesis testing (t-test, f-test). 9. The Matrix Approach to Linear Regression Model.

Course Synopsis

The main objectives of the course are to: • Introduce students to basic econometrics techniques and to prepare them to do their own applied work • Emphasis the use and interpretation of single equation regression techniques in formulating and testing microeconomics and macroeconomics hypotheses.

Course Learning Outcomes

• Know the basic principles of econometric modelling and analysis • Be able to understand both the fundamental techniques and wide array of applications involving linear regression estimation • Conduct hypothesis testing