A Note on Amemiya's Nonlinear Two-stage Least Squares Estimators PDF Download
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Author: Richard Emeric Quandt
Publisher:
ISBN:
Category : Economics, Mathematical
Languages : en
Pages : 24
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Author: Richard Emeric Quandt
Publisher:
ISBN:
Category : Economics, Mathematical
Languages : en
Pages : 24
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Author: Takeshi Amemiya
Publisher:
ISBN:
Category :
Languages : en
Pages : 22
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Book Description
Author: Roger Koenker
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 36
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Book Description
Author: Takeshi Amemiya
Publisher: Cambridge University Press
ISBN: 9780521662468
Category : Business & Economics
Languages : en
Pages : 472
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Book Description
This collection investigates parametric, semiparametric, nonparametric, and nonlinear estimation techniques in statistical modeling.
Author: A. Ronald Gallant
Publisher: John Wiley & Sons
ISBN: 047031737X
Category : Mathematics
Languages : en
Pages : 633
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Book Description
A comprehensive text and reference bringing together advances in the theory of probability and statistics and relating them to applications. The three major categories of statistical models that relate dependent variables to explanatory variables are covered: univariate regression models, multivariate regression models, and simultaneous equations models. Methods are illustrated with worked examples, complete with figures that display code and output.
Author: Donald W. K. Andrews
Publisher: Cambridge University Press
ISBN: 1139444603
Category : Business & Economics
Languages : en
Pages : 589
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Book Description
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.
Author: Zvi Griliches
Publisher: Elsevier
ISBN: 9780444861856
Category : Econometrics
Languages : en
Pages : 804
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Book Description
The Handbook is a definitive reference source and teaching aid for econometricians. It examines models, estimation theory, data analysis and field applications in econometrics. Comprehensive surveys, written by experts, discuss recent developments at a level suitable for professional use by economists, econometricians, statisticians, and in advanced graduate econometrics courses.
Author: H. J. Bierens
Publisher: Springer Science & Business Media
ISBN: 3642455298
Category : Mathematics
Languages : en
Pages : 211
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Book Description
This Lecture Note deals with asymptotic properties, i.e. weak and strong consistency and asymptotic normality, of parameter estimators of nonlinear regression models and nonlinear structural equations under various assumptions on the distribution of the data. The estimation methods involved are nonlinear least squares estimation (NLLSE), nonlinear robust M-estimation (NLRME) and non linear weighted robust M-estimation (NLWRME) for the regression case and nonlinear two-stage least squares estimation (NL2SLSE) and a new method called minimum information estimation (MIE) for the case of structural equations. The asymptotic properties of the NLLSE and the two robust M-estimation methods are derived from further elaborations of results of Jennrich. Special attention is payed to the comparison of the asymptotic efficiency of NLLSE and NLRME. It is shown that if the tails of the error distribution are fatter than those of the normal distribution NLRME is more efficient than NLLSE. The NLWRME method is appropriate if the distributions of both the errors and the regressors have fat tails. This study also improves and extends the NL2SLSE theory of Amemiya. The method involved is a variant of the instrumental variables method, requiring at least as many instrumental variables as parameters to be estimated. The new MIE method requires less instrumental variables. Asymptotic normality can be derived by employing only one instrumental variable and consistency can even be proved with out using any instrumental variables at all.
Author: Michael D. McCarthy
Publisher:
ISBN:
Category :
Languages : en
Pages : 34
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Author: Dimitris Hatzinikolaou
Publisher:
ISBN:
Category :
Languages : en
Pages :
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