Description

Book Synopsis
This book examines the consequences of misspecifications from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference.

Trade Review
'... 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

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.

Estimation Inference and Specification Analysis

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    A Paperback by Halbert White

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      Book details

      Publisher Cambridge University Press
      Published 6/28/1996 12:00:00 AM
      ISBN-13 9780521574464
      978-0521574464
      ISBN-10 0521574463
      Also in:
      Econometrics and economic statistics Probability and statistics

      Description

      Book Synopsis
      This book examines the consequences of misspecifications from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference.

      Trade Review
      '... 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

      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.

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