Incluye bibliografías.

I. Information Processing Recovery: 1. The process of econometric information recovery ; 2. Probability-econometric models -- 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 -- 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 -- 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 -- V. Generalized regression models: 14. Regression models with a known general noise covariance matrix ; 15. Regression models with an unknown general noise covariance matrix -- 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 -- VII. Model discovery: 18. Model discovery : the problem of variable selection and conditioning ; 19. Model discovery: the problem of noise covariance matrix specification -- VIII. Special Econometric Topics: 20. Qualitative-censored response models ; 21. Introduction to nonparametric density and regression analysis -- 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 -- X. Epilogue: Appendix: introduction to computer simulation and resampling methods. "