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Full Description
This integrated textbook and CD-ROM develop step by step a modern approach to econometric problems. Aimed at upper-level undergraduates, graduate students, and professionals, they describe the principles and procedures for processing and recovering information from samples of economic data. In the real world such data are usually limited or incomplete, and the parameters sought are unobserved and not subject to direct observation or measurement. The text provides a complete working knowledge of a rich set of estimation and inference tools for mastery of such data, including traditional likelihood based and non-traditional non-likelihood based procedures, that can be used in conjunction with the computer to address economic problems. The CD-ROM contains reviews of probability theory and principles of classical estimation and inference in text-searchable electronic documents, a review ofhandling ill-posed inverse problems, an interactive Matrix Review Manual with Gauss software, an electronic Examples Manual, and solutions to the questions and problems in the text. An electronic tutorial is available separately.
Contents
Part I. Information Processing Recoveryinformation 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.
