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Spatial econometrics /
Spatial Econometrics provides a modern, powerful and flexible skillset to early career researchers interested in entering this rapidly expanding discipline. It articulates the principles and current practice of modern spatial econometrics and spatial statistics, combining rigorous depth of presentat...
| Clasificación: | Libro Electrónico |
|---|---|
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom :
Academic Press,
[2017]
�2017 |
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Spatial econometrics / |c Harry Kelejian, Gianfranco Piras. |
| 260 | |a London, United Kingdom : |b Academic Press, |c [2017] | ||
| 264 | 4 | |c �2017 | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |6 880-01 |a Front Cover; Spatial Econometrics; Copyright; Contents; List of Empirical Illustrations; List of Figures; List of Tables; Preface; Acknowledgements; 1 Spatial Models: Basic Issues; 1.1 Illustrations Involving Spatial Interactions; 1.2 Concept of a Neighbor and the Weighting Matrix; 1.3 Some Different Ways to Specify Spatial Weighting Matrices; 1.4 Typical Weighting Matrices in Computer Studies; Suggested Problems; 2 Specification and Estimation; 2.1 The General Model; 2.1.1 Triangular Arrays; 2.1.2 Ger�sgorin's Theorem and Weighting Matrices. | |
| 505 | 8 | |a 3 Spillover Effects in Spatial Models3.1 Effects Emanating From a Given Unit; 3.2 Emanating Effects of a Uniform Worsening of Fundamentals; 3.3 Vulnerability of a Given Unit to Spillovers; Suggested Problems; 4 Predictors in Spatial Models; 4.1 Preliminaries on Expectations; 4.2 Information Sets and Predictors of the Dependent Variable; 4.3 Mean Squared Errors of the Predictors; Suggested Problems; 5 Problems in Estimating Weighting Matrices; 5.1 The Spatial Model; 5.2 Shortcomings of Selection Based on R2; 5.3 An Extension to Nonlinear Spatial Models. | |
| 505 | 8 | |a 5.4 R2 Selection in the Multiple Panel Case Suggested Problems; 6 Additional Endogenous Variables: Possible Nonlinearities; 6.1 Introductory Comments; 6.2 Identi cation and Estimation: A Linear System; 6.3 A Corresponding Nonlinear Model; 6.4 Estimation in the Nonlinear Model; 6.5 Large Sample and Related Issues; 6.6 Generalizations and Special Points to Note; 6.7 Applications to Spatial Models; 6.8 Problems With MLE; Suggested Problems; 7 Bayesian Analysis; 7.1 Introductory Comments; 7.2 Fundamentals of the Bayesian Approach; 7.3 Learning and Prejudgment Issues. | |
| 505 | 8 | |a 7.4 Comments on Uninformed Priors7.5 Applications and Limiting Cases; 7.6 Properties of the Multivariate t; 7.7 Useful Sampling Procedures in Bayesian Analysis; 7.8 The Spatial Lag Model and Gibbs Sampling; 7.9 Bayesian Posterior Odds and Model Selection; 7.10 Problems With the Bayesian Approach; Suggested Problems; 8 Pretest and Sample Selection Issues in Spatial Analysis; 8.1 Introductory Comments; 8.2 A Preliminary Result; 8.3 Illustrations; 8.4 Mean Squared Errors; 8.5 Pretesting in Spatial Models: Large Sample Issues; 8.6 Final Comments on Pretesting; 8.7 A Related Issue: Data Selection. | |
| 520 | |a Spatial Econometrics provides a modern, powerful and flexible skillset to early career researchers interested in entering this rapidly expanding discipline. It articulates the principles and current practice of modern spatial econometrics and spatial statistics, combining rigorous depth of presentation with unusual depth of coverage. Introducing and formalizing the principles of, and 'need' for, models which define spatial interactions, the book provides a comprehensive framework for almost every major facet of modern science. Subjects covered at length include spatial regression models, weighting matrices, estimation procedures and the complications associated with their use. The work particularly focuses on models of uncertainty and estimation under various complications relating to model specifications, data problems, tests of hypotheses, along with systems and panel data extensions which are covered in exhaustive detail. Extensions discussing pre-test procedures and Bayesian methodologies are provided at length. Throughout, direct applications of spatial models are described in detail, with copious illustrative empirical examples demonstrating how readers might implement spatial analysis in research projects. Designed as a textbook and reference companion, every chapter concludes with a set of questions for formal or self--study. Finally, the book includes extensive supplementing information in a large sample theory in the R programming language that supports early career econometricians interested in the implementation of statistical procedures covered. | ||
| 650 | 0 | |a Econometrics. | |
| 650 | 0 | |a Space in economics. | |
| 650 | 6 | |a �Econom�etrie. |0 (CaQQLa)201-0025541 | |
| 650 | 6 | |a Espace (�Economie politique) |0 (CaQQLa)201-0083796 | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Economics |x General. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Reference. |2 bisacsh | |
| 650 | 7 | |a Econometrics |2 fast |0 (OCoLC)fst00901574 | |
| 650 | 7 | |a Space in economics |2 fast |0 (OCoLC)fst01127736 | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Economics |x General. |2 gnd | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Reference. |2 gnd | |
| 720 | |a Kelejian, Harry. | ||
| 720 | |a Piras, Gianfranco. | ||
| 776 | 0 | 8 | |i Print version: |z 9780128133873 |z 0128133872 |w (OCoLC)974699238 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128133873 |z Texto completo |
| 880 | 8 | |6 505-01/(S |a 2.1.3 Normalization to Ensure a Continuous Parameter Space2.1.4 An Important Condition in Large Sample Analysis; 2.2 Estimation: Various Special Cases; 2.2.1 Estimation When ρ1=ρ2=0; 2.2.2 Estimation When ρ1=0 and ρ20; 2.2.2.1 Maximum Likelihood Estimation: ρ1=0, ρ20; 2.2.3 Assumptions of the General Model; 2.2.4 A Generalized Moments Estimator of ρ2; 2.3 IV Estimation of the General Model; 2.4 Maximum Likelihood Estimation of the General Model; 2.5 An Identi cation Fallacy; 2.6 Time Series Procedures Do Not Always Carry Over; Appendix A2 Proofs for Chapter 2; Suggested Problems. | |