Part II. Regression Model-estimation and Inference

3. The multivariate normal linear regression model: ML estimation
4. The multivariate normal linear regression model: inference
5. The linear semiparametric regression model: least squares estimation
6. The linear semiparametric regression model: inference

Part III. Extremum Estimators and Nonlinear and Nonnormal Regression Models

7. Extremum estimation and inference
8. The nonlinear semiparametric regression model: estimation and inference
9. Nonlinear and nonnormal parametric regression models

Part IV. Avoiding the Parametric Likelihood

10. Stochastic regressors and moment-based estimation
11. Quasi-maximum likelihood and estimating equations
12. Empirical likelihood estimation and inference
13. Information theoretic-entropy approaches to estimation and inference

Part V. Generalized Regression Models

14. Regression models with a known general noise covariance matrix
15. Regression models with an unknown general noise covariance matrix

Part VI. Simultaneous Equation Probability Models and General Moment-Based Estimation and Inference

16. Generalized moment-based estimation and inference
17. Simultaneous equations econometric models: estimation and inference

Part VII. Model Discovery

18. Model discovery: the problem of variable selection and conditioning
19. Model discovery: the problem of noise covariance matrix specification

Part VIII. Special Econometric Topics

20. Qualitative-censored response models
21. Introduction to nonparametric density and regression analysis

Part IX. Bayesian Estimation and Inference

22. Bayesian estimation: general principles with a regression focus
23. Alternative Bayes formulations for the regression model
24. Bayesian inference

Part X. Epilogue

Appendix: introduction to computer simulation and resampling methods