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An Information Theoretic Approach to Econometrics

An Information Theoretic Approach to Econometrics


  • Date Published: December 2011
  • availability: In stock
  • format: Paperback
  • isbn: 9780521689731


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About the Authors
  • This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure–likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family.

    • Develops the solution to stochastic ill-posed inverse problems within traditional sampling theory estimation and inference
    • Includes sampling experiments to illustrate finite sample performance
    • Provides a basis for handling computational problems, elucidating new estimation and inference paradigms in econometrics
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    Product details

    • Date Published: December 2011
    • format: Paperback
    • isbn: 9780521689731
    • length: 248 pages
    • dimensions: 228 x 152 x 13 mm
    • weight: 0.33kg
    • contains: 13 b/w illus. 7 tables
    • availability: In stock
  • Table of Contents

    1. Econometric information recovery
    Part I. Traditional Parametric and Semiparametric Probability Models: Estimation and Inference:
    2. Formulation and analysis of parametric and semiparametric linear models
    3. Method of moments, GMM, and estimating equations
    Part II. Formulation and Solution of Stochastic Inverse Problems:
    4. A stochastic-empirical likelihood inverse problem: formulation and estimation
    5. A stochastic-empirical likelihood inverse problem: inference
    6. Kullback-Leibler information and the maximum empirical exponential likelihood
    Part III. A Family of Minimum Discrepancy Estimators:
    7. The Cressie-Read family of divergence measures and likelihood functions
    8. Cressie-Read-MEL-type estimators in practice: evidence of estimation and inference sampling performance
    Part IV. Binary Discrete Choice MPD-EML Econometric Models:
    9. Family of distribution functions for the binary response-choice model
    10. Estimation and inference for the binary response model based on the MPD family of distributions
    Part V. Optimal Convex Divergence:
    11. Choosing the optimal divergence under quadratic loss
    12. Epilogue.

  • Authors

    George G. Judge, University of California, Berkeley

    Ron C. Mittelhammer, Washington State University

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