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.
General
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