This 2005 collection pushed forward the research frontier in four areas of theoretical econometrics. Editor(s): Andrews, Donald W. K.; Stock, James H. Num Pages: 588 pages, black & white illustrations. BIC Classification: KCHS. Category: (P) Professional & Vocational. Dimension: 158 x 230 x 36. Weight in Grams: 880. . 2010. Reprint. paperback. . . . .

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Bibliographic Details

Title: Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg
Publisher: Cambridge University Press
Publication Date: 2010
Language: English
ISBN 10: 052115474X
ISBN 13: 9780521154741
Binding: Soft cover
Condition: New

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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.

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