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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 inference. 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.
He then investigates the limiting distribution of the MLE under misspecification, the conditions under which MLE efficiency is not affected 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.
- 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.
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Previews available in: English
Subjects
Econometric models, EconometricsShowing 3 featured editions. View all 3 editions?
Edition | Availability |
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1
Estimation, Inference and Specification Analysis
2013, Cambridge University Press
in English
1139052233 9781139052238
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2
Estimation, Inference and Specification Analysis
1996, Cambridge University Press
in English
0521574463 9780521574464
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3
Estimation, inference, and specification analysis
1994, Cambridge University Press
in English
0521252806 9780521252805
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Book Details
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Includes bibliographical references (p. 359-367) and indexes.
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- Created April 1, 2008
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