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Spatial econometrics için kapak resmi
Başlık:
Spatial econometrics
Yazar:
Kelejian, Harry H.
ISBN:
9780128133873
Yazar Ek Girişi:
Fiziksel Tanımlama:
xxi, 435 pages ; 23 cm.
İçerik:
Note continued: 2.4. Maximum Likelihood Estimation of the General Model --2.5. An Identification Fallacy --2.6. Time Series Procedures Do Not Always Carry Over --Appendix A2 Proofs for Chapter 2 --Suggested Problems --3. Spillover Effects in Spatial Models --3.1. 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 pectations --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. Note continued: 6.2. Identification 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 Priors --7.5. 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. Note continued: 8.5. Pretesting in Spatial Models: Large Sample Issues --8.6. Final Comments on Pretesting --8.7.A Related Issue: Data Selection --8.8. Endogenous Data Selection Issues --8.9. Exogenous Data Selection Issues --Suggested Problems --9. HAC Estimation of VC Matrices --9.1. Introductory Comments on Heteroskedasticity --9.2. Spatially Correlated Errors: Illustrations --9.3. Assumptions and HAC Estimation --9.4. Kernel Functions That Satisfy Assumption 9.8 --9.5. HAC Estimation With Multiple Distances --9.6. Nonparametric Error Terms and Maximum Likelihood: Serious Problems --Suggested Problems --10. Missing Data and Edge Issues --10.1. Introductory Comments --10.2.A Simple Model and Limits of Information --10.3. Incomplete Samples and External Data --10.4. The Spatial Error Model: IV and ML With Missing Data --10.5.A More General Spatial Model --10.6. Spatial Error Models: Be Careful What You Do --Appendix A10 Proofs for Chapter 10. Note continued: Suggested Problems --11. Tests for Spatial Correlation --11.1. Introductory Comments: Occam's Razor --11.2. Some Preliminary Issues on a Quadratic Form --11.3. The Moran I Test: A Basic Model --11.4. An Important Independence Result --11.5. Application: The Moments of the Moran I --11.6. Generalized Moran I Tests: Qualitative Models and Spatially Lagged Dependent Variable Models --11.7. Lagrangian Multiplier Tests --11.8. The Wald Test --11.9. Spatial Correlation Tests: Comments and Caveats --Suggested Problems --12. Nonnested Models and the J-Test --12.1. Introductory Comments --12.2. The Null Model: Nonparametric Error Terms --12.3. The Alternative Models --12.4. Two Predictors --12.5. The Augmented Equation and the J-Test --12.6. The J-Test: SAR Error Terms --12.7.J-Test and Nonlinear Alternatives --Suggested Problems --13. Endogenous Weighting Matrices: Specifications and Estimation --13.1. Introductory Comments --13.2. The Model. Note continued: 13.3. Issues Concerning Error Term Specification --13.4. Further Specifications --13.5. The Instrument Matrix --13.6. Estimation and Inference --Suggested Problems --14. Systems of Spatial Equations --14.1. Introductory Comments --14.2. An Illustrative Two-Equations Model --14.3. The Model With Nonparametric Error Terms --14.4. Assumptions of the Model --14.5. Interpretation of the Assumptions --14.6. Estimation and Inference --14.7. The Model With SAR Error Terms --14.8. Estimation and Inference: GS3SLS --Suggested Problems --15. Panel Data Models --15.1. Introductory Comments --15.2. Some Important Preliminaries --15.3. The Random Effects Model --15.4.A Generalization of the Random Effects Model --15.5. The Fixed Effects Model --15.6.A Generalization of the Fixed Effects Model --15.7. Tests of Panel Models: The J-Test --Suggested Problems --A. Introduction to Large Sample Theory --A.1. An Intuitive Introduction. Note continued: A.2. Application of the Large Sample Result in (A.1.6) --A.3. More Formalism: Convergence in Probability --A.4. Khinchine's Theorem --A.5. An Important Property of Convergence in Probability --A.6.A Matrix Illustration of Consistency --A.7. eneralizations of Slutsky-Type Results --A.8.A Note on the Least Squares Model --A.9. Convergence in Distribution --A.10. Results on Convergence in Distribution --A.11. Convergence in Distribution: Slutsky-Type Results --A.12. Constructing Finite Sample Approximations --A.13.A Result Relating to Nonlinear Functions of Estimators --A.14. Orders in Probability --A.15. Triangular Arrays: A Central Limit Theorem --B. Spatial Models in R --B.1. Introduction --B.2. Introductory Tools --B.3. Reading Data and Creating Weights --B.4. Estimating Spatial Models.
Özet:
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. -- Provided by publisher
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