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.
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.
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.
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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