9780521574464 - Estimation, Inference Specification (Econometric Society Monographs, 22, Band 22) von White (5 Ergebnisse)

Zustand: Good. Volume 22. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,600grams, ISBN:0521574463.

Zustand: New. In.

Paperback. Zustand: Brand New. reprint edition. 392 pages. 9.00x6.25x0.75 inches. In Stock.

Zustand: New. 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. Series Editor(s): Chesher, Andrew; Jackson, Matthew. Series: Econometric Society Monographs. Num Pages: 396 pages, black & white illustrations. BIC Classification: KCH; PBT. Category: (P) Professional & Vocational. Dimension: 229 x 153 x 21. Weight in Grams: 614. . 2008. Revised ed. paperback. . . . . Books ship from the US and Ireland.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.