The Maximum Likelihood Stage Least Squares Estimator in the Nonlinear Simultaneous Equations Model PDF Download
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Author: Takeshi Amemiya
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The consistency and the asymptotic normality of the maximum likelihood estimator in the general nonlinear simultaneous equation model are proved. It is shown that the proof depends on the assumption of normality unlike in the linear simultaneous equation model. It is proved that the maximum likelihood estimator is asymptotically more efficient than the nonlinear three-stage least squares estimator if the specification is correct, However, the latter has the advantage of being consistent even when the normality assumption is removed. Hausrnan' s instrumental-variable-interpretation of the maximum likelihood estimator is extended to the general nonlinear simultaneous equation model.
Author: Takeshi Amemiya
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
The consistency and the asymptotic normality of the maximum likelihood estimator in the general nonlinear simultaneous equation model are proved. It is shown that the proof depends on the assumption of normality unlike in the linear simultaneous equation model. It is proved that the maximum likelihood estimator is asymptotically more efficient than the nonlinear three-stage least squares estimator if the specification is correct, However, the latter has the advantage of being consistent even when the normality assumption is removed. Hausrnan' s instrumental-variable-interpretation of the maximum likelihood estimator is extended to the general nonlinear simultaneous equation model.
Author: Takeshi Amemiya
Publisher:
ISBN:
Category : Differential equations, Nonlinear
Languages : en
Pages : 0
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Book Description
"The consistency and the asymptotic normality of the maximum likelihood estimator in the general nonlinear simultaneous equation model are proved. It is shown that the proof depends on the assumption of normality unlike in the linear simultaneous equation model. It is proved that the maximum likelihood estimator is asymptotically more efficient than the nonlinear three-stage least squares estimator if the specification is correct, However, the latter has the advantage of being consistent even when the normality assumption is removed. Hausrnan' s instrumental-variable-interpretation of the maximum likelihood estimator is extended to the general nonlinear simultaneous equation model"--NBER website
Author: Phoebus J. Dhrymes
Publisher: Springer Science & Business Media
ISBN: 1461243025
Category : Business & Economics
Languages : en
Pages : 411
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Book Description
This book is intended for second year graduate students and professionals who have an interest in linear and nonlinear simultaneous equations mod els. It basically traces the evolution of econometrics beyond the general linear model (GLM), beginning with the general linear structural econo metric model (GLSEM) and ending with the generalized method of mo ments (GMM). Thus, it covers the identification problem (Chapter 3), maximum likelihood (ML) methods (Chapters 3 and 4), two and three stage least squares (2SLS, 3SLS) (Chapters 1 and 2), the general nonlinear model (GNLM) (Chapter 5), the general nonlinear simultaneous equations model (GNLSEM), the special ca'3e of GNLSEM with additive errors, non linear two and three stage least squares (NL2SLS, NL3SLS), the GMM for GNLSEIVl, and finally ends with a brief overview of causality and re lated issues, (Chapter 6). There is no discussion either of limited dependent variables, or of unit root related topics. It also contains a number of significant innovations. In a departure from the custom of the literature, identification and consistency for nonlinear models is handled through the Kullback information apparatus, as well as the theory of minimum contrast (MC) estimators. In fact, nearly all estimation problems handled in this volume can be approached through the theory of MC estimators. The power of this approach is demonstrated in Chapter 5, where the entire set of identification requirements for the GLSEM, in an ML context, is obtained almost effortlessly, through the apparatus of Kullback information.
Author: Hag-Soo Kim
Publisher:
ISBN:
Category :
Languages : en
Pages : 258
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Book Description
Abstract: Semiparametric and nonparametric estimation methods have been employed in the estimation of many important econometrics models. Among many interesting econometrics models, we consider nonlinear simultaneous equations models that are known not to be adaptive, which implies that we cannot estimate the parameter vector as efficient asymptotically as if the true distribution of structural errors were known. The nonlinear full information maximum likelihood estimator is in general inconsistent unless the assumed density for the structural errors is the true one. The nonlinear three stage least squares estimator, while robust against misspecification of the error distribution, is not efficient.
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: Peter Ole Anderson
Publisher:
ISBN:
Category : Economics, Mathematical
Languages : en
Pages : 132
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Book Description
The basic problem considered here is the estimation of the coefficients in a simultaneous system of linear equations. Maximum likelihood, linearized maximum likelihood, two-stage least-squares and three-stage least-squares methods have primarily been studied for the case of linear constraints among the coefficients. In this paper the case of nonlinear constraints is considered. It is shown that by linearizing the nonlinear constraints and using a three-stage least-squares procedure one can obtain similar large sample, and also small sigma, results. (Author).
Author: Stephen M. Goldfeld
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 52
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Book Description
The small sample properties of certain estimators of the coefficients of systems of simultaneous nonlinear equations are investigated. Sampling experiments are used in connection with two specific nonlinear models. The estimating methods investigated comprise direct least squares, various forms of two-stage least squares and full-information maximum likelihood. The relative performances of the various methods are evaluated on the basis of informal comparisons of their respective mean absolute errors and root mean square errors and also by more formal tests of significance. Direct least squares is found to be, as expected, the worst estimating method. The other two methods are rather more comparable with full-information maximum likelihood holding the edge for both theoretical and experimental reasons. (Author).
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: Thomas B. Fomby
Publisher: Springer Science & Business Media
ISBN: 1441987460
Category : Business & Economics
Languages : en
Pages : 637
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Book Description
This book had its conception in 1975in a friendly tavern near the School of Businessand PublicAdministration at the UniversityofMissouri-Columbia. Two of the authors (Fomby and Hill) were graduate students of the third (Johnson), and were (and are) concerned about teaching econometrics effectively at the graduate level. We decided then to write a book to serve as a comprehensive text for graduate econometrics. Generally, the material included in the bookand itsorganization have been governed by the question, " Howcould the subject be best presented in a graduate class?" For content, this has meant that we have tried to cover " all the bases " and yet have not attempted to be encyclopedic. The intended purpose has also affected the levelofmathematical rigor. We have tended to prove only those results that are basic and/or relatively straightforward. Proofs that would demand inordinant amounts of class time have simply been referenced. The book is intended for a two-semester course and paced to admit more extensive treatment of areas of specific interest to the instructor and students. We have great confidence in the ability, industry, and persistence of graduate students in ferreting out and understanding the omitted proofs and results. In the end, this is how one gains maturity and a fuller appreciation for the subject in any case. It is assumed that the readers of the book will have had an econometric methods course, using texts like J. Johnston's Econometric Methods, 2nd ed.
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.
