1. Introduction to Econometrics:

What is econometrics? – Steps in econometrics analysis – Structure of economic data – Causality. 

2. Classical Linear Regression Model (CLRM): Assumptions

Bivariate and multivariate ordinary least squares models – Assumptions for estimation and hypothesis testing of OLS estimators. 

3. CLRM: Estimation and Hypothesis Testing

Deriving OLS estimates – Properties of OLS estimators – Distribution of OLS estimators – Hypothesis testing for single population parameter (t-test) – Confidence Intervals – Hypothesis testing for linear combination of parameters – Testing multiple linear restrictions 

4. CLRM: Further Issues

Effects of data scaling on OLS estimates – Functional forms – Prediction and residual analysis 

5. CLRM: Violation of assumptions

Multicollinearity – Autocorrelation - Heteroscedasticity 

6. Econometric Models with Qualitative Explanatory Variables

Single dummy explanatory variable – Using dummy variables for multiple categories – interaction involving dummy variables

7. Specification and Measurement Errors

Functional form misspecification, Proxy variables for unobserved explanatory variables – Properties of OLS with measurement error – Missing data problem  

References: 

1. Dougherty, C. 2011. Introduction to Econometrics (4^th ed.). Oxford University Press.

2. Gujarati, D.N. and Sangeetha. 2007. Basic Econometrics (Special Indian Edition), 4/e. McGraw-Hill.

3. Hill, R. C., W.E.Griffiths, and G.G. Judge. 2001. Undergraduate Econometrics (2^nd ed.). Wiley.

4. Maddala, G.S. and Lahiri K. 2009. Introduction to Econometrics.Wiley-India.

5. Wooldridge, J.M.2009. Econometrics. Cengage Learning.