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E-raamat: Spatial Econometrics

(Department of Economics, University of Maryland, College Park, MD, USA), (The Bush School of Business and Economics, The Catholic University of America, Washington, D.C., USA)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 20-Jul-2017
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128133927
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Spatial EconometricsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 20-Jul-2017
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128133927
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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.

  • Combines advanced theoretical foundations with cutting-edge computational developments in R
  • Builds from solid foundations, to more sophisticated extensions that are intended to jumpstart research careers in spatial econometrics
  • Written by two of the most accomplished and extensively published econometricians working in the discipline
  • Describes fundamental principles intuitively, but without sacrificing rigor
  • Provides empirical illustrations for many spatial methods across diverse field
  • Emphasizes a modern treatment of the field using the generalized method of moments (GMM) approach
  • Explores sophisticated modern research methodologies, including pre-test procedures and Bayesian data analysis

Muu info

Supports graduate and PhD students seeking to develop skills in the derivation, analysis and implementation of tractable econometric models using spatial dimensions
Preface xvii
Acknowledgements xxi
1 Spatial Models: Basic Issues
1.1 Illustrations Involving Spatial Interactions
1(2)
1.2 Concept of a Neighbor and the Weighting Matrix
3(1)
1.3 Some Different Ways to Specify Spatial Weighting Matrices
4(4)
1.4 Typical Weighting Matrices in Computer Studies
8(4)
Suggested Problems
10(2)
2 Specification and Estimation
2.1 The General Model
12(10)
2.1.1 Triangular Arrays
14(1)
2.1.2 Gersgorin's Theorem and Weighting Matrices
15(3)
2.1.3 Normalization to Ensure a Continuous Parameter Space
18(2)
2.1.4 An Important Condition in Large Sample Analysis
20(2)
2.2 Estimation: Various Special Cases
22(21)
2.2.1 Estimation When ρ1 = ρ2 = 0
22(5)
2.2.2 Estimation When ρ1 = 0 and ρ2 ≠ 0
27(1)
2.2.2.1 Maximum Likelihood Estimation: ρ1 = 0, ρ2 ≠ 0
28(5)
2.2.3 Assumptions of the General Model
33(4)
2.2.4 A Generalized Moments Estimator of ρ2
37(6)
2.3 IV Estimation of the General Model
43(3)
2.4 Maximum Likelihood Estimation of the General Model
46(3)
2.5 An Identification Fallacy
49(1)
2.6 Time Series Procedures Do Not Always Carry Over
50(11)
Appendix A2 Proofs for
Chapter 2
52(6)
Suggested Problems
58(3)
3 Spillover Effects in Spatial Models
3.1 Effects Emanating From a Given Unit
61(3)
3.2 Emanating Effects of a Uniform Worsening of Fundamentals
64(2)
3.3 Vulnerability of a Given Unit to Spillovers
66(5)
Suggested Problems
69(2)
4 Predictors in Spatial Models
4.1 Preliminaries on Expectations
71(4)
4.2 Information Sets and Predictors of the Dependent Variable
75(5)
4.3 Mean Squared Errors of the Predictors
80(7)
Suggested Problems
86(1)
5 Problems in Estimating Weighting Matrices
5.1 The Spatial Model
87(1)
5.2 Shortcomings of Selection Based on R2
88(2)
5.3 An Extension to Nonlinear Spatial Models
90(1)
5.4 R2 Selection in the Multiple Panel Case
91(6)
Suggested Problems
95(2)
6 Additional Endogenous Variables: Possible Nonlinearities
6.1 Introductory Comments
97(1)
6.2 Identification and Estimation: A Linear System
98(2)
6.3 A Corresponding Nonlinear Model
100(2)
6.4 Estimation in the Nonlinear Model
102(4)
6.5 Large Sample and Related Issues
106(2)
6.6 Generalizations and Special Points to Note
108(5)
6.7 Applications to Spatial Models
113(7)
6.8 Problems With MLE
120(3)
Suggested Problems
121(2)
7 Bayesian Analysis
7.1 Introductory Comments
123(1)
7.2 Fundamentals of the Bayesian Approach
124(1)
7.3 Learning and Prejudgment Issues
125(2)
7.4 Comments on Uninformed Priors
127(2)
7.5 Applications and Limiting Cases
129(9)
7.6 Properties of the Multivariate t
138(2)
7.7 Useful Sampling Procedures in Bayesian Analysis
140(14)
7.8 The Spatial Lag Model and Gibbs Sampling
154(4)
7.9 Bayesian Posterior Odds and Model Selection
158(2)
7.10 Problems With the Bayesian Approach
160(3)
Suggested Problems
161(2)
8 Pretest and Sample Selection Issues in Spatial Analysis
8.1 Introductory Comments
163(1)
8.2 A Preliminary Result
163(1)
8.3 Illustrations
164(4)
8.4 Mean Squared Errors
168(1)
8.5 Pretesting in Spatial Models: Large Sample Issues
169(6)
8.6 Final Comments on Pretesting
175(2)
8.7 A Related Issue: Data Selection
177(1)
8.8 Endogenous Data Selection Issues
178(2)
8.9 Exogenous Data Selection Issues
180(5)
Suggested Problems
182(3)
9 HAC Estimation of VC Matrices
9.1 Introductory Comments on Heteroskedasticity
185(4)
9.2 Spatially Correlated Errors: Illustrations
189(4)
9.3 Assumptions and HAC Estimation
193(7)
9.4 Kernel Functions That Satisfy Assumption 9.8
200(5)
9.5 HAC Estimation With Multiple Distances
205(1)
9.6 Nonparametric Error Terms and Maximum Likelihood: Serious Problems
206(3)
Suggested Problems
208(1)
10 Missing Data and Edge Issues
10.1 Introductory Comments
209(1)
10.2 A Simple Model and Limits of Information
210(8)
10.3 Incomplete Samples and External Data
218(1)
10.4 The Spatial Error Model: IV and ML With Missing Data
219(2)
10.5 A More General Spatial Model
221(9)
10.6 Spatial Error Models: Be Careful What You Do
230(7)
Appendix A10 Proofs for
Chapter 10
232(3)
Suggested Problems
235(2)
11 Tests for Spatial Correlation
11.1 Introductory Comments: Occam's Razor
237(1)
11.2 Some Preliminary Issues on a Quadratic Form
238(2)
11.3 The Moran I Test: A Basic Model
240(5)
11.4 An Important Independence Result
245(1)
11.5 Application: The Moments of the Moran I
246(1)
11.6 Generalized Moran I Tests: Qualitative Models and Spatially Lagged Dependent Variable Models
247(11)
11.7 Lagrangian Multiplier Tests
258(8)
11.8 The Wald Test
266(1)
11.9 Spatial Correlation Tests: Comments and Caveats
267(4)
Suggested Problems
270(1)
12 Nonnested Models and the J-Test
12.1 Introductory Comments
271(1)
12.2 The Null Model: Nonparametric Error Terms
272(1)
12.3 The Alternative Models
272(1)
12.4 Two Predictors
273(2)
12.5 The Augmented Equation and the J-Test
275(2)
12.6 The J-Test: SAR Error Terms
277(1)
12.7 J-Test and Nonlinear Alternatives
278(5)
Suggested Problems
281(2)
13 Endogenous Weighting Matrices: Specifications and Estimation
13.1 Introductory Comments
283(1)
13.2 The Model
284(1)
13.3 Issues Concerning Error Term Specification
284(1)
13.4 Further Specifications
285(2)
13.5 The Instrument Matrix
287(2)
13.6 Estimation and Inference
289(4)
Suggested Problems
292(1)
14 Systems of Spatial Equations
14.1 Introductory Comments
293(1)
14.2 An Illustrative Two-Equations Model
294(1)
14.3 The Model With Nonparametric Error Terms
294(2)
14.4 Assumptions of the Model
296(1)
14.5 Interpretation of the Assumptions
297(1)
14.6 Estimation and Inference
298(2)
14.7 The Model With SAR Error Terms
300(2)
14.8 Estimation and Inference: CS3SLS
302(5)
Suggested Problems
306(1)
15 Panel Data Models
15.1 Introductory Comments
307(1)
15.2 Some Important Preliminaries
307(1)
15.3 The Random Effects Model
308(8)
15.4 A Generalization of the Random Effects Model
316(5)
15.5 The Fixed Effects Model
321(8)
15.6 A Generalization of the Fixed Effects Model
329(7)
15.7 Tests of Panel Models: The J-Test
336(7)
Suggested Problems
341(2)
A Introduction to Large Sample Theory
A.1 An Intuitive Introduction
343(2)
A.2 Application of the Large Sample Result in (A.1.6)
345(1)
A.3 More Formalism: Convergence in Probability
345(3)
A.4 Khinchine's Theorem
348(1)
A.5 An Important Property of Convergence In Probability
349(1)
A.6 A Matrix Illustration of Consistency
349(1)
A.7 Generalizations of Slutsky-Type Results
350(2)
A.8 A Note on the Least Squares Model
352(1)
A.9 Convergence in Distribution
352(1)
A.10 Results on Convergence in Distribution
353(1)
A.11 Convergence in Distribution: Slutsky-Type Results
354(1)
A.12 Constructing Finite Sample Approximations
355(2)
A.13 A Result Relating to Nonlinear Functions of Estimators
357(2)
A.14 Orders in Probability
359(3)
A.15 Triangular Arrays: A Central Limit Theorem
362(1)
B Spatial Models in R
B.1 Introduction
363(1)
B.2 Introductory Tools
364(8)
B.3 Reading Data and Creating Weights
372(4)
B.4 Estimating Spatial Models
376(13)
Answer Manual 389(28)
References 417(12)
Index 429
Harry Kelejian is Professor of Economics at the University of Maryland. He has held academic positions at Princeton and New York Universities. He has also been a Visiting Professor at the Institute for Advanced Studies in Vienna, Austria (1979, 2005, 2006); at the Australian National University in Canberra (1982); and at the University of Konstanz in Germany (1997). He was selected in 1995 for the Prentice Hall of Fame Economist Series. He publishes widely in applied and theoretical econometrics. Gianfranco Piras is an Associate Professor of Economics at the Busch School of Business and Economics at The Catholic University of America. Formerly, he was a Research Assistant Professor at the Regional Research Institute at West Virginia University. He has also spent time at the Department of City and Regional Planning at Cornell University, the Regional Economic Application Laboratory at the University of Illinois at Urbana-Champaign, and at the GeoDa center at Arizona State University. He held a position of Assistant Professor at the Universidad Catolica del Norte in Chile. Dr. Piras is a member of the editorial board of Letters of Spatial and Resource Sciences. Dr. Piras research interests include spatial econometrics and statistics, urban and regional economics, computational methods and software development. He is one of the developers of the R software for statistical computing and he is currently working on two main libraries, for the estimation of spatial panel data models (SPLM), and for the application of GM methods in spatial econometrics (SPHET).
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