Spatial econometrics / Harry Kelejian, Gianfranco Piras.

Format:

Book

Author/Creator:

Kelejian, Harry H., author.

Piras, Gianfranco, author.

Contributor:

ScienceDirect (Online service)

Language:

English

Subjects (All):

Econometrics.

Space in economics.

Physical Description:

1 online resource (xxi, 435 pages.)

Place of Publication:

London, United Kingdom : Academic Press, an imprint of Elsevier, [2017]

Contents:

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

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

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

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

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

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

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

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

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

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

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

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

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. Generalizations 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.

Notes:

Includes bibliographical references (pages 417-427) and index.

Electronic reproduction. Amsterdam Available via World Wide Web.

Description based on print version record.

ISBN:

9780128133927 (electronic bk.)

0128133929 (electronic bk.)

Publisher Number:

90100352374

Access Restriction:

Restricted for use by site license.