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Econometric Foundations

Econometric Foundations
Pack with CD-ROM

textbook
  • Date Published: July 2000
  • availability: Unavailable - out of print
  • format: Mixed media product
  • isbn: 9780521623940

Mixed media product

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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.

    • Most sophisticated econometrics text available, with integrated text and CD-ROM and user-defined interactive computer-based examples
    • George Judge is world-class author of fourteen other books in econometrics
    • Step by step explanation of all major econometrics tools, including a Sampling Theory and Bayesian approach to inference, Monte Carlo sampling procedures, Markov chains and bootstrap methods
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    Reviews & endorsements

    "This is an excellent graduate-level text on the foundations of econometric estimation and inference...Most importantly, the authors...devote significant effort to explaining what econometric science is all about...This is an excellent econometric text. It provedes the necessary econometric tools and answers, while outlining the difficulties in data analyses. It not only provides the reader with most recent econometric and statistic methods, it also provides a unified approach to econometrics, thereby making it easier for students to comprehend, apply, and practice...Given its low price, it is definitely a bargain and should be on the desk of every graduate student and practitioner." JASA

    "The book has an innovative approach to integrating electronic resources and software applications into its presentation....the authors are not shy of including the software code directly into the body of the text where they wish to emphasize the ease of applicability of the procedures discussed.Econometric Foundations has a bold, novel perspective that is often rewarding." Ian Preston, Times Higher Education Supplement Internet Service

    "The authors of Econometric Foundations are to be congratulated for their comprehensive and clear presentation of old and new econometric methods along with many interesting and relevant applications. This fine blend of theory and application makes this text particularly useful and appealing." Arnold Zellner, University of Chicago

    "This graduate text breaks new ground in both content and delivery. Not only is much of the content on the frontier, but also Bayesian and classical approaches are simultaneously developed in a creative manner." N. Eugene Savin, University of Iowa

    "To a degree not previously accomplished in a textbook, this masterful volume unifies econometric theory and modern approaches to its application. Spanning state-of-the-art advances in sampling theoretic and Bayesian inference, the book also integrates concepts from systems theory by emphasizing the logic of the inverse problem." William A. Barnett, Washington University in St. Louis

    "The book by Mittelhammer, Judge and Miller provides a sound introduction to classical econometric models and methods and in addition it covers a range of special topics such as semiparametric and nonparametric regression, maximum entropy and moment-based estimation techniques. Moreover, it offers a strong treatment of Bayesian methods. It emphasizes methods for data without time series characteristics and will be a strong competitor in the relevant section of the market for econometrics textbooks." Helmut LÜtkepohl, Humboldt-Universität zu Berlin

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    Product details

    • Date Published: July 2000
    • format: Mixed media product
    • isbn: 9780521623940
    • length: 784 pages
    • dimensions: 262 x 183 x 42 mm
    • weight: 1.555kg
    • contains: 5 b/w illus. 32 tables
    • availability: Unavailable - out of print
  • 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.

  • Resources for

    Econometric Foundations

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

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  • Authors

    Ron C. Mittelhammer, Washington State University

    George G. Judge, University of California, Berkeley

    Douglas J. Miller, Purdue University, Indiana

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