Synopses & Reviews
Econometric Foundations establishes a new paradigm for teaching econometric problems to talented upper-level undergraduates, graduate students, and professionals. The complete package (text, accompanying CD-ROM, and electronic guide) provides relevance, clarity, and organization to those wishing to acquaint themselves with the principles and procedures for information processing and recovery 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. Econometric Foundations fully provides an operational understanding of a rich set of estimation and inference tools to master 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 accompanying CD-ROM contains reviews of probability theory, principles of classical estimation and inference, and handling of ill-posed inverse problems in text-searchable electronic documents, an interactive Matrix Review manual with GAUSS LIGHT software, and an electronic Examples Manual. A separate Guide, which may be accessed through the Internet, further enhances the student's mastery of the topics by providing solutions guides to the questions and problems in the text. This text, CD-ROM, and electronic guide package make Econometric Foundations the most up-to-date and comprehensive learning resource available.
Synopsis
This textbook and accompanying CD-ROM develop step by step a modern approach to econometric problems.
Synopsis
This course provides a complete working knowledge of a rich set of estimation and inference tools, 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 accompanying CD-ROM offers further reading, manuals, software, and solutions. An electronic tutorial is available separately.
Synopsis
This course develops step by step a modern approach to econometric problems. Aimed at upper-level undergraduates, graduate students, and professionals, it describes the principles and procedures for processing and recovering information from samples of economic data. The text provides a complete working knowledge of a rich set of estimation and inference tools, 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 offers further reading, manuals, software, and solutions. An electronic tutorial is available separately.
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.