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
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1. Spatial Models: Basic Issues 2. Specification and Estimation 3. Spill Over Effects in Spatial Models 4. Predictors in Spatial Models 5. Problems in Estimating Weighting Matrices 6. Additional Endogenous Variables: and Possible Nonlinearities 7. Bayesian Analysis 8. Pre-test and Sample Selection Issues in Spatial Analysis 9. HAC Estimation of V-C Matrices 10. Missing Data and Edge Issues 11. Tests for Spatial Correlation 12. Non-Nest Models and the J-Test 13. Endogenous Weighting Matrices: Specification and Estimation 14. Systems of Spatial Equations 15. Panel Data Models Appendix A: Introduction to large sample theory Appendix B: Spatial Models in R
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).