Suchergebnisse

Front Cover -- Spatial Econometrics -- Copyright -- Contents -- List of Empirical Illustrations -- List of Figures -- List of Tables -- Preface -- Acknowledgements -- 1 Spatial Models: Basic Issues -- 1.1 Illustrations Involving Spatial Interactions -- 1.2 Concept of a Neighbor and the Weighting Matrix -- 1.3 Some Different Ways to Specify Spatial Weighting Matrices -- 1.4 Typical Weighting Matrices in Computer Studies -- Suggested Problems -- 2 Specification and Estimation -- 2.1 The General Model -- 2.1.1 Triangular Arrays -- 2.1.2 Geršgorin's Theorem and Weighting Matrices -- 2.1.3 Normalization to Ensure a Continuous Parameter Space -- 2.1.4 An Important Condition in Large Sample Analysis -- 2.2 Estimation: Various Special Cases -- 2.2.1 Estimation When ρ1=ρ2=0 -- 2.2.2 Estimation When ρ1=0 and ρ20 -- 2.2.2.1 Maximum Likelihood Estimation: ρ1=0, ρ20 -- 2.2.3 Assumptions of the General Model -- 2.2.4 A Generalized Moments Estimator of ρ2 -- 2.3 IV Estimation of the General Model -- 2.4 Maximum Likelihood Estimation of the General Model -- 2.5 An Identi cation 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 Expectations -- 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.

This thesis is an attempt to obtain further insight into the role of spatial and dynamic linkages in the field of Economics given the crucial need for a better understanding of the fundamental processes behind the spatial and temporal correlation patterns observable in the economic data. To date, most theoretical economic models and econometric studies have treated units of analysis as isolated entities, ignoring the spatial characteristics of the data and the potential role of space in modulating the economic evolution of countries, regions, municipalities, etc. In this regard, the essence of spatial economic analysis is that space matters. This implies that what happens in one economic unit of analysis is linked to what happens in neighboring economic units. In a spatial economic modeling framework, the spatial dimension and geographical arrangement of interacting economic agents are key drivers of economic processes and their final outcomes. The recognition of the wide range of interconnections between the interacting agents in economics requires to accommodate such interdependence in the modeling process and in order to verify models of social and spatial interaction, these spatial effects need to be explicitly accounted for. Failure to take into account spatial dependence and spatial heterogeneity in econometric models leads to major estimation problems because the coefficient estimates will be biased, inconsistent and/or inefficient. A distinct and innovative feature of this research is the use of static and dynamic spatial panel data estimation techniques for the empirical testing and validation of the theoretical models developed in the different chapters. This methodological approach is particularly appropriate for the analysis of economic phenomena from an integrated space-time perspective because it allows to model spillover, feedback and diffusion effects among the study units. Frequentist Spatial Econometrics modeling tools are complemented with Bayesian Spatial Econometrics and Relative Importance metrics in order to gain knowledge about the type of connectivity structures, the underlying spatial processes behind the observable data and to carry out inference in the relevance of the different factors explaining disparities among spatial units in time. The structure of this thesis consists of four self-contained chapters. Chapter 1 analyzes the volatility-regional growth nexus in a sample of European regions. To that end, a model of stochastic neoclassical growth with spatial interdependence is developed. In this framework, the economic growth rate of a particular region is affected not only by its own degree of volatility but also by the output fluctuations experienced by the remaining regions. In order to investigate the empirical validity of this result, the link between volatility and economic growth is examined in a sample of 272 European regions over the period 1991-2011 using a variety of static spatial pane specifications including spatial fixed effects. The results suggest the existence of a robust negative link between volatility and growth. Chapter 2 investigates regional development dynamics in a sample of 254 NUTS 2 European Union regions over the period 2000–2010. To that end, a new version of the Regional Lisbon Index (RLI) containing changes with respect the index developed by Dijkstra is proposed. The RLI employment, education and R&D indicators. Targets for these indicators are related to an action and economic development plan for the EU regions and have been incorporated into European Regional Policy programming. The analysis of regional development is based on the estimation of the spatial Durbin model. Different specifications of the spatial weights matrix describing the spatial arrangement are compared by means of Spatial Bayesian Econometrics techniques. The salient finding of this chapter is that the main drivers of the RLI growth rate are technological capital, infrastructures and employment growth. Chapter 3 analyzes unemployment differentials in 241 European regions during the period 2000-2011. To that end, a theoretical model with substantive spatial interactions among regions is developed. The solution implies a Dynamic Spatial Durbin Model specification including regional and institutional level factors as explanatory variables. In conjunction with dynamic-spatial panel estimates, relative importance metrics are used to quantify the effect of regional disequilibrium, equilibrium and national level factors. Relative importance analysis suggests that during the pre-crisis period unemployment disparities were mainly driven by regional level equilibrium factors. Nevertheless, labor market institutions are of major importance to explain increasing disparities during 2009-2011. Chapter 4 looks into the nature of fiscal policy interactions in local fiscal policy in Spain. This study extends traditional spatial spillover models of government spending by including dynamic effects in order to test the relevance of the incremental budget hypothesis stemming from political science research. The theoretical model developed in this study points out to an empirical specification including simultaneous and lagged endogenous interactions among the sample of municipalities, as well as exogenous interaction effects. To that end, a Dynamic Spatial Durbin panel data model is used to quantify the relevance of spatial spillovers and diffusion effects over time. Using annual data for a sample of 1230 Spanish municipalities during 2000 to 2012, it is observed that: there are significant positive simultaneous spatial spillovers in different government expenditure categories and that the incremental hypothesis stemming from political science has a greater explanatory power than that of spatial spillovers. ; Programa de Doctorado en Economía, Empresa y Derecho (RD 99/2011) ; Ekonomiako, Enpresako eta Zuzenbideko Doktoretza Programa (ED 99/2011)

"This is the most recently developed book in Spatial Econometrics which cover important models and estimation methods. Its coverage is rather broad, and some of the topics covered have only been developed in the recent econometric literature in spatial econometrics. The book summarizes our devoted efforts on spatial econometrics that represent joint contributions with former PhD advisees from the Ohio State University in Columbus, Ohio, USA. The coverage is comprehensive and there are a total of sixteen chapters from basic statistics and statistical theory of linear-quadratic forms, law of large numbers (LLN) and central limit theory (CLT) on martingales to nonlinear spatial mixing and spatial near-epoch dependence theories, which can justify the statistic inferences for various spatial models and their estimation. New estimation and testing approaches in empirical likelihood and general empirical likelihood, and Bootstrapping are presented. Model selection is also discussed in this book. In addition to the popular spatial autoregressive models, there are chapters on multivariate SAR models, simultaneous SAR models, and panel dynamic spatial model models. Recent econometric developments on intertemporal spatial models with rational expectations and on flows data in trade theory will also be included. In terms of statistics, classical estimation, testing and inference are the main concerns, and we provide classical inference for the justification of Bayesian simulation approaches"--

"The objective of this book is to make a link between existing quantitative approaches... and the manner in which we can generalize thess approaches to cases where the available data for analysis have a spatial dimension."--

1: Introduction -- 2: The Scope of Spatial Econometrics -- 3: The Formal Expression of Spatial Effects -- 4: A Typology of Spatial Econometric Models -- 5: Spatial Stochastic Processes: Terminology and General Properties -- 6: The Maximum Likelihood Approach to Spatial Process Models -- 7: Alternative Approaches to Inference in Spatial Process Models -- 8: Spatial Dependence in Regression Error Terms -- 9: Spatial Heterogeneity -- 10: Models in Space and Time -- 11: Problem Areas in Estimation and Testing for Spatial Process Models -- 12: Operational Issues and Empirical Applications -- 13: Model Validation and Specification Tests in Spatial Econometric Models -- 14: Model Selection in Spatial Econometric Models -- 15: Conclusions -- References.

Despite spatial statistics and spatial econometrics both being recent sprouts of the general tree ""spatial analysis with measurement""—some may remember the debate after WWII about ""theory without measurement"" versus ""measurement without theory""—several general themes have emerged in the pertaining literature. But exploring selected other fields of possible interest is tantalizing, and this is what the authors intend to report here, hoping that they will suscitate interest in the methodologies exposed and possible further applications of these methodologies. The authors hope that reactions about their publication will ensue, and they would be grateful to reader(s) motivated by some of the research efforts exposed hereafter letting them know about these experiences.

This resource describes a website and playlist of YouTube videos using open source software (R, GeoDa, and QGIS) designed to help get scholars up and running with analyzing their own data. In the series sample data, handouts, code, and map files are provided. The course covers the basics of integrating data into a spatial data set, contiguity and spatial correlation, doing basic spatial regressions in GeoDa, and doing more sophisticated specification tests and regressions in R.