Testing Non-nested Multivariate Nonlinear Regression Models with and Without Specification of the Error Distribution PDF Download

Author: Victor Manuel-Armando Aguirre-Torres
Publisher:
ISBN:
Category :
Languages : en
Pages : 156

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Author: Mohammad Hashem Pesaran
Publisher:
ISBN:
Category :
Languages : en
Pages :

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Author:
Publisher:
ISBN:
Category : Dissertations, Academic
Languages : en
Pages : 782

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Author: M. H. Pesaran
Publisher:
ISBN:
Category :
Languages : en
Pages : 18

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Author: Institute of Mathematical Statistics
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 904

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Author: L. Anselin
Publisher: Springer Science & Business Media
ISBN: 9401577994
Category : Business & Economics
Languages : en
Pages : 295

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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: Anil K. Bera
Publisher:
ISBN: 9780868311623
Category : Linear models (Statistics)
Languages : en
Pages : 21

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Author: David A. Belsley
Publisher: John Wiley & Sons
ISBN: 0470748907
Category : Mathematics
Languages : en
Pages : 514

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Book Description
Handbook of Computational Econometrics examines the state of the art of computational econometrics and provides exemplary studies dealing with computational issues arising from a wide spectrum of econometric fields including such topics as bootstrapping, the evaluation of econometric software, and algorithms for control, optimization, and estimation. Each topic is fully introduced before proceeding to a more in-depth examination of the relevant methodologies and valuable illustrations. This book: Provides self-contained treatments of issues in computational econometrics with illustrations and invaluable bibliographies. Brings together contributions from leading researchers. Develops the techniques needed to carry out computational econometrics. Features network studies, non-parametric estimation, optimization techniques, Bayesian estimation and inference, testing methods, time-series analysis, linear and nonlinear methods, VAR analysis, bootstrapping developments, signal extraction, software history and evaluation. This book will appeal to econometricians, financial statisticians, econometric researchers and students of econometrics at both graduate and advanced undergraduate levels.

Author: Michael J. Cullan
Publisher:
ISBN:
Category : Bootstrap (Statistics)
Languages : en
Pages : 76

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Book Description
Statistical model selection using the Akaike Information Criterion (AIC) and similar criteria is a useful tool for comparing multiple and non-nested models without the specification of a null model, which has made it increasingly popular in the natural and social sciences. Despite their common usage, model selection methods are not driven by a notion of statistical confidence, so their results entail an unknown degree of uncertainty. This paper introduces a general framework which extends notions of Type-I and Type-II error to model selection. A theoretical method for controlling Type-I error using Difference of Goodness of Fit (DGOF) distributions is given, along with a bootstrap approach that approximates the procedure. Results are presented for simulated experiments using normal distributions, random walk models, nested linear regression, and nonnested regression including nonlinear models. Tests are performed using an R package developed by the author which will be made publicly available on journal publication of research results.

Author:
Publisher:
ISBN:
Category : Dissertation abstracts
Languages : en
Pages : 564

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