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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 |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.