Asymptotic Properties of Maximum Likelihood Estimators in a Nonlinear Regression Model with Unknown Parameters in the Disturbance Covariance Matrix PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Asymptotic Properties of Maximum Likelihood Estimators in a Nonlinear Regression Model with Unknown Parameters in the Disturbance Covariance Matrix PDF full book. Access full book title Asymptotic Properties of Maximum Likelihood Estimators in a Nonlinear Regression Model with Unknown Parameters in the Disturbance Covariance Matrix by R. D. H. Heijmans. Download full books in PDF and EPUB format.
Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
Get Book Here
Book Description
Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 25
Get Book Here
Book Description
Author: Henk Don
Publisher:
ISBN:
Category : Regression analysis
Languages : en
Pages : 26
Get Book Here
Book Description
Author: Franco Peracchi
Publisher: John Wiley & Sons
ISBN: 0471987646
Category : Business & Economics
Languages : en
Pages : 706
Get Book Here
Book Description
In Econometrics the author has provided a text that bridges the gap between classical econometrics (with an emphasis on linear methods such as OLS, GLS and instrumental variables) and some of the key research areas of the last few years, including sampling problems, nonparametric methods and panel data analysis. Designed for advanced undergraduates and postgraduate students of the subject, Econometrics provides rigorous, yet accessible, coverage of the subject. Key features include: * A unified approach to statistical estimation emphasising the analogy (or bootstrap) principle * An introduction to bootstrap and jackknife methods for assessing the accuracy of an estimator * Detailed discussion of nonparametric methods for estimating density and regression of functions * Emphasis on diagnostic procedures and on prediction criteria for evaluating the results fo statistical analysis * An introduction to linear exponential family and generalized linear models * A thorough discussion of robustness in statistical sense
Author: Mary Kathleen Vickers
Publisher:
ISBN:
Category : Asymptotes
Languages : en
Pages : 312
Get Book Here
Book Description
Four theorems are proven, which simplify the application to econometric models of Weiss's theorem on asymptotic properties of maximum likelihood estimators in nonstandard cases. The theorems require, roughly: the uniform convergence in any compact sets of the unknown parameters of the expection of the Hessian matrix of the log likelihood function; and the uniform convergence to 0 in the same sense of the variance of the same quantities. The fourth theorem allows one to conclude that the optimal properties hold on an image set of the parameters when the map satisfies certain smoothness conditions, and the first three theorems are satisfied for the original parameter set. These four theorems are applied to autoregressive models, nonlinear models, systems of equations, and probit and logit models to infer optimal asymptotic properties. (Author).
Author:
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 850
Get Book Here
Book Description
Author: L. Anselin
Publisher: Springer Science & Business Media
ISBN: 9401577994
Category : Business & Economics
Languages : en
Pages : 295
Get Book Here
Book Description
Spatial econometrics deals with spatial dependence and spatial heterogeneity, critical aspects of the data used by regional scientists. These characteristics may cause standard econometric techniques to become inappropriate. In this book, I combine several recent research results to construct a comprehensive approach to the incorporation of spatial effects in econometrics. My primary focus is to demonstrate how these spatial effects can be considered as special cases of general frameworks in standard econometrics, and to outline how they necessitate a separate set of methods and techniques, encompassed within the field of spatial econometrics. My viewpoint differs from that taken in the discussion of spatial autocorrelation in spatial statistics - e.g., most recently by Cliff and Ord (1981) and Upton and Fingleton (1985) - in that I am mostly concerned with the relevance of spatial effects on model specification, estimation and other inference, in what I caIl a model-driven approach, as opposed to a data-driven approach in spatial statistics. I attempt to combine a rigorous econometric perspective with a comprehensive treatment of methodological issues in spatial analysis.
Author: Takeshi Amemiya
Publisher: Harvard University Press
ISBN: 9780674005600
Category : Business & Economics
Languages : en
Pages : 540
Get Book Here
Book Description
The main features of this text are a thorough treatment of cross-section models—including qualitative response models, censored and truncated regression models, and Markov and duration models—and a rigorous presentation of large sample theory, classical least-squares and generalized least-squares theory, and nonlinear simultaneous equation models.
Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 84
Get Book Here
Book Description
Author: R. D. H. Heijmans
Publisher:
ISBN:
Category :
Languages : en
Pages : 84
Get Book Here
Book Description
Author: Stanford University. Department of Statistics
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 556
Get Book Here
Book Description
The problem considered is the estimation of the parameters in the mixed model of the analysis of variance, assuming normality of the random effects and errors. Both asymptotic properties of such estimates as the size of the design increases and numerical procedures for their calculation are discussed. Estimation is carried out by the method of maximum likelihood. It is shown that there is a sequence of roots of the likelihood equations which is consistent, asymptotically normal and asymptotically efficient in the sense of attaining the Cramer-Rao lower bound for the covariance matrix as the size of the design increases. This is accomplished using a Taylor series expansion of the log-likelihood. (Modified author abstract).
