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by Ron C. Mittelhammer, George G. Judge and Douglas Miller

Edition: 00Copyright: 2000

Publisher: Cambridge University Press

Published: 2000

International: No

Ron C. Mittelhammer, George G. Judge and Douglas Miller

Edition: 00
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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.

**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 non normal 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 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.

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Summary

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.

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 non normal 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 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.

Publisher Info

Publisher: Cambridge University Press

Published: 2000

International: No

Published: 2000

International: No

**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 non normal 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 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.