Estimation, Inference and Specification Analysis
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. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification.
1100938801
Estimation, Inference and Specification Analysis
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. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification.
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Estimation, Inference and Specification Analysis

Estimation, Inference and Specification Analysis

by Halbert White
Estimation, Inference and Specification Analysis

Estimation, Inference and Specification Analysis

by Halbert White

Overview

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. The analysis concludes with an examination of methods by which the possibility of misspecification can be empirically investigated and offers a variety of tests for misspecification.

Product Details

ISBN-13: 9780521252805
Publisher: Cambridge University Press
Publication date: 09/30/1994
Series: Econometric Society Monographs , #22
Pages: 396
Product dimensions: 6.18(w) x 9.29(h) x 1.18(d)

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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