Table of Contents

Part I. Information Processing Recovery: 1. The process of econometric information recovery; 2. Probability-econometric models; 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.