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This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihoodbased methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximumlikelihood estimation, treats the interpretation of the maximumlikelihood 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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