Basic econometrics / Damodar N. Gujarati, Dawn C Porter and Sangeetha Gunasekar

Introduction;
Part-1: Single-equation regression models;
1. The nature of regression analysis;
2. Two-variable regression analysis: some basic ideas;
3. Two-variable regression model: the problem of estimation;
4. Classical normal linear regression model (CNLRM);
5. Two-variable regression: interval estimation and hypothesis testing;
6. Extensions of the two-variable linear regression model;
7. Multiple regression analysis: the problem of estimation;
Multiple regression analysis: the problem of inference;
9. Dummy variable regression models;
Part-2: Re;axing the assumptions of the classical model;
10. Multicollinearity: what happens if the regression are correlated;
11. Heteroscedasticity: what happens if the error variance is non constant?
12. Autocorrelation: what happens if the error terms are correlated?;
13. Econometric modeling: model specification and diagnostic testing;
Part-3: Topics in econometrics;
14. Nonlinear regression models;
15. Qualitative response regression models;
16. Panel data regression models;
17. Dynamic econometric models: autoregressive and distributed-lag models;
Part-4: Simultaneous-equation models and time series econometrics;
18. Simultaneous-equation models;
19. The identification problems;
20. Simultaneous-equation methods;
21. Time series econometrics: some basic concepts;
22. Time series econometrics: forecasting;