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Asymptotic Theory for Econometricians 2nd Edition
Author(s): Halbert White (Author)
- Publisher: Emerald Publishing Limited
- Publication Date: October 11, 2000
- Edition: 2nd
- Language: English
- Print length: 264 pages
- ISBN-10: 0127466525
- ISBN-13: 9780127466521
Book Description
This book provides the tools and concepts necessary to study the behavior of econometric estimators and test statistics in large samples. An econometric estimator is a solution to an optimization problem; that is, a problem that requires a body of techniques to determine a specific solution in a defined set of possible alternatives that best satisfies a selected object function or set of constraints. Thus, this highly mathematical book investigates situations concerning large numbers, in which the assumptions of the classical linear model fail. Economists, of course, face these situations often. It includes completely revised chapter seven on functional central limit theory and its applications, specifically unit root regression, spurious regression, and regression with cointegrated processes. It includes updated material on: central limit theory; asymptotically efficient instrumental variables estimation; estimation of asymptotic covariance matrices; efficient estimation with estimated error covariance matrices; and efficient IV estimation.
Editorial Reviews
From the Back Cover
The amount of financial data created every day by world stock markets, world governments, financial institutions, and other sources, is increasing at an enormous rate. Economists and financial analysts need tools to manage these large sets of data in a timely and accurate way. Classical linear models of economics have failed to deal with such large amounts of data, and asymptotic theory is the tool that economists have come to rely on for this type of data management.
Large sample theory and the fundamental tools of asymptotic theory converge in this thoroughly revised edition of Asymptotic Theory for Econometricians. New material on functional central limit theory and its applications, material on cointegration, and many small points make this Revised Edition a comprehensive and unified treatment of large sample theory. The scope of the book remains the same as that of the First Edition, with sufficient material to fill a full year’s course work. This edition also contains updated material on asymptotically efficient instrumental variables estimation, efficient estimation with estimated error covariance matrices, and efficient IV estimation. Exercise solutions have also been updated and expanded.
Asymptotic Theory for Econometricians 2nd Edition is intended both as a reference for practicing econometricians and financial analysts and as a textbook for graduate students taking courses in econometrics beyond the introductory level. It assumes that the reader is familiar with the basic concepts of probability and statistics as well as with calculus and linear algebra, and that the reader also has a good understanding of the classical linear model.
Large sample theory and the fundamental tools of asymptotic theory converge in this thoroughly revised edition of Asymptotic Theory for Econometricians. New material on functional central limit theory and its applications, material on cointegration, and many small points make this Revised Edition a comprehensive and unified treatment of large sample theory. The scope of the book remains the same as that of the First Edition, with sufficient material to fill a full year’s course work. This edition also contains updated material on asymptotically efficient instrumental variables estimation, efficient estimation with estimated error covariance matrices, and efficient IV estimation. Exercise solutions have also been updated and expanded.
Asymptotic Theory for Econometricians 2nd Edition is intended both as a reference for practicing econometricians and financial analysts and as a textbook for graduate students taking courses in econometrics beyond the introductory level. It assumes that the reader is familiar with the basic concepts of probability and statistics as well as with calculus and linear algebra, and that the reader also has a good understanding of the classical linear model.
