Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg

Andrews, Donald W. K. (Editor)/ Stock, James H. (Editor)

ISBN 10: 052115474X / ISBN 13: 9780521154741

Published by Cambridge Univ Pr, 2010

About this Item

reprint edition. 573 pages. 8.75x5.75x1.25 inches. In Stock. This item is printed on demand.

Seller Inventory # __052115474X

Bibliographic Details

• Title: Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg
• Publisher: Cambridge Univ Pr
• Publication Date: 2010
• Language: English
• ISBN 10: 052115474X
• ISBN 13: 9780521154741
• Binding: Paperback
• Condition: Brand New
• Weight: 0.86 kilograms

About this title

Synopsis

This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.

Book Description

The chapters in this 2005 text cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference.