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Look Inside Estimation, Inference and Specification Analysis

Estimation, Inference and Specification Analysis


Part of Econometric Society Monographs

  • Date Published: November 1994
  • availability: Available
  • format: Hardback
  • isbn: 9780521252805

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About the Authors
  • This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models, and gives the conditions under which parameters of interest can be consistently estimated despite misspecification, and the consequences of misspecification, for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. Although the theory presented in the book is motivated by econometric problems, its applicability is by no means restricted to economics. Subject to defined limitations, the theory applies to any scientific context in which statistical analysis is conducted using approximate models.

    • Highly acclaimed and bestselling book now available in paperback for the first time
    • Well-known author at the cutting edge of the field
    • Latest paperback addition to the successful Econometric Society Monographs series
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    Reviews & endorsements

    '... contains much material of interest to econometricians … a useful source book for researchers, instructors and graduate students and essential reading for those interested in the effects of misspecification.' Econometric Theory

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    Product details

    • Date Published: November 1994
    • format: Hardback
    • isbn: 9780521252805
    • length: 396 pages
    • dimensions: 236 x 157 x 30 mm
    • weight: 0.728kg
    • availability: Available
  • Table of Contents

    1. Introductory remarks
    2. Probability densities, likelihood functions and the quasi-maximum likelihood estimator
    3. Consistency of the QMLE
    4. Correctly specified models of density
    5. Correctly specified models of conditional expectation
    6. The asymptotic distribution of the QMLE and the information matrix equality
    7. Asymptotic efficiency
    8. Hypothesis testing and asymptotic covariance matrix estimation
    9. Specification testing via m-tests
    10. Applications of m-testing
    11. Information matrix testing
    12. Conclusion
    Appendix 1. Elementary concepts of measure theory and the Radon-Nikodym theorem
    Appendix 2. Uniform laws of large numbers
    Appendix 3. Central limit theorems.

  • Author

    Halbert White, University of California, San Diego

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