9780521154741 - Identification and Inference for Econometric Models: Essays in Honor of Thomas Rothenberg (10 Ergebnisse)

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Zustand: New. 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. . . . .

Zustand: New. 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. . . . . Books ship from the US and Ireland.

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

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Paperback. Zustand: Brand New. reprint edition. 573 pages. 8.75x5.75x1.25 inches. In Stock. This item is printed on demand.

Paperback. Zustand: new. Paperback. 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. 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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.