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.

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