1 New Directions in Spatial Econometrics: Introduction.- 1.1 Introduction.- 1.2 Spatial Effects in Regression Models.- 1.2.1 Specification of Spatial Dependence.- 1.2.2 Spatial Data and Model Transformations.- 1.3 Spatial Effects in Limited Dependent Variable Models.- 1.4 Heterogeneity and Dependence in Space-Time Models.- 1.5 Future Directions.- References.- I-A: Spatial Effects in Linear Regression Models Specification of Spatial Dependence.- 2 Small Sample Properties of Tests for Spatial Dependence in Regression Models: Some Further Results.- 2.1 Introduction.- 2.2 Tests for Spatial Dependence.- 2.2.1 Null and Alternative Hypotheses.- 2.2.2 Tests for Spatial Error Dependence.- 2.2.3 Tests for Spatial Lag Dependence.- 2.3 Experimental Design.- 2.4 Results of Monte Carlo Experiments.- 2.4.1 Empirical Size of the Tests.- 2.4.2 Power of Tests Against First Order Spatial Error Dependence.- 2.4.3 Power of Tests Against Spatial Autoregressive Lag Dependence.- 2.4.4 Power of Tests Against Second Order Spatial Error Dependence.- 2.4.5 Power of Tests Against a SARMA (1,1) Process.- 2.5 Conclusions.- Acknowledgements.- References.- Appendix 1: Tables.- 3 Spatial Correlation: A Suggested Alternative to the Autoregressive Model.- 3.1 Introduction.- 3.2 The Spatial AR Model of Autocorrelation.- 3.3 The Singularity of (I - pM).- 3.3.1 Theoretical Issues.- 3.3.2 Independent Corroborative Evidence.- 3.4 The Parameter Space.- 3.5 A Suggested Variation of the Spatial AR Model.- 3.5.1 The Suggested Model.- 3.5.2 Some Limiting Correlations.- 3.5.3 A Generalization.- 3.6 Suggestions for Further Work.- Acknowledgements.- References.- Appendix 1: Spatial Weighting Matrices.- 4 Spatial Autoregressive Error Components in Travel Flow Models: An Application to Aggregate Mode Choice.- 4.1 Introduction.- 4.2 The First-Order Spatially Autoregressive Error Components Formulation.- 4.3 Estimation Issues.- 4.4 Empirical Example.- 4.4.1 An Illustration Based on Synthetic Data.- 4.5 Conclusions.- References.- I-B: Spatial Effects in Linear Regression Models Spatial Data and Model Transformations.- 5 The Impacts of Misspecified Spatial Interaction in Linear Regression Models.- 5.1 Introduction.- 5.2 Aggregation and the Identification of Spatial Interaction.- 5.3 Experimental Design.- 5.3.1 Sample Size.- 5.3.2 Spatial Interaction Structures.- 5.3.3 Spatial Models and Parameter Space.- 5.3.4 Test Statistics and Estimators.- 5.3.5 Forms of Misspecification.- 5.4 Empirical Results.- 5.4.1 Size of Tests Under the Null.- 5.4.2 Power of Tests.- 5.4.3 Misspecification Effects on the Power of Tests for Spatial Dependence.- 5.4.4 Sensitivity of Parameter Estimation to Specification of Weight Matrix.- 5.4.5 Impact of Misspecification of Weight Matrix on Estimation.- 5.5 General Inferences References.- 6 Computation of Box-Cox Transform Parameters: A New Method and its Application to Spatial Econometrics.- 6.1 Introduction.- 6.2 The Elasticity Method: Further Elaboration.- 6.2.1 Linearization Bias.- 6.2.2 Discretization Bias.- 6.2.3 Specification Bias.- 6.3 The One Exogenous Variable Test.- 6.4 An Application to Spatial Econometrics.- 6.5 The Multiple Exogenous Variable Computation.- 6.6 Conclusions.- References.- 7 Data Problems in Spatial Econometric Modeling.- 7.1 Introduction.- 7.2 Data for Spatial Econometric Analysis.- 7.3 Data Problems in Spatial Econometrics.- 7.4 Methodologies for Handling Data Problems.- 7.4.1 Influential Cases in the Standard Regression Model.- 7.4.2 Influential Cases in a Spatial Regression Model.- 7.4.3 An Example.- 7.5 Implementing Methodologies.- References.- 8 Spatial Filtering in a Regression Framework: Examples Using Data on Urban Crime, Regional Inequality, and Government Expenditures.- 8.1 Introduction.- 8.2 Rationale for a Spatial Filter.- 8.3 The Gi Statistic.- 8.4 The Filtering Procedure.- 8.5 Filtering Variables: Three Examples.- 8.5.1 Example 1: Urban Crime.- 8.5.2 Example 2: Regional Inequality.- 8.5.3 Example 3: Government Expenditures.- >8.6 Conclusions.- >Acknowledgments.- References.- II: Spatial Effects in Limited Dependent Variable Models.- 9 Spatial Effects in Probit Models: A Monte Carlo Investigation.- 9.1 Introduction.- 9.2 Sources of Heteroscedasticity.- 9.3 Heteroscedastic Probit.- 9.4 Monte Carlo Design.- 9.5 Tests.- 9.6 Monte Carlo Results.- 9.7 Conclusions.- References.- Appendix 1: Monte Carlo Results.- Appendix 2: Heteroscedastic Probit Computer Programs.- Appendix 3: Monte Carlo Computer Programs.- 10 Estimating Logit Models with Spatial Dependence.- 10.1 Introduction.- 10.1.1 Model.- 10.2 Simulation Example.- 10.3 Conclusions.- >References.- Appendix 1: Gauss Program for Finding ML Estimates.- Appendix 2: Gauss Program to Estimate Asymptotic Variances of ML Estimates.- 11 Utility Variability within Aggregate Spatial Units and its Relevance to Discrete Models of Destination Choice.- 11.1 Introduction.- 11.2 Theoretical Background.- 11.3 Estimation of the Maximum Utility Model.- 11.4 Model Specifications and Simulations.- 11.4.1 Specification Issues.- 11.4.2 Description of Simulation Method.- 11.4.3 Results.- 11.5 Conclusions.- Acknowledgement.- References.- III: Heterogeneity and Dependence in Space-Time Models.- 12 The General Linear Model and Spatial Autoregressive Models.- 12.1 Introduction.- 12.2 The GLM.- 12.3 Data Preprocessing.- 12.3.1 Analysis of the 1964 Benchmark Data.- 12.3.2 Evaluation of Missing USDA Values Estimation.- >12.4 Implementation of the Spatial Statistical GLM.- 12.4.1 Preliminary Spatial Analysis of Milk Yields: AR Trend Surface GLMs.- 12.4.2 AR GLM Models for the Repeated Measures Case.- 12.4.3 A Spatially Adjusted Canonical Correlation Analysis of the Milk Production Data.- 12.5 Conclusions.- >References.- >Appendix 1: SAS Computer Code to Compute the Popular Spatial Autocorrelation Indices.- Appendix 2: SAS Code for Estimating Missing Values in the 1969 Data Set.- Appendix 3: SAS Code for 1969 USDA Data Analysis.- 13 Econometric Models and Spatial Parametric Instability: Relevant Concepts and an Instability Index.- 13.1 Introduction.- 13.2 The Expansion Method.- 13.3 Parametric Instability.- 13.3.1 Example.- 13.4 Conclusions.- 13.4.1 Instability Measures: Scope.- 13.4.2 Instability Measures: Significance.- References.- 14 Bayesian Hierarchical Forecasts for Dynamic Systems: Case Study on Backcasting School District Income Tax Revenues.- 14.1 Introduction.- 14.2 Literature Review.- 14.3 The C-MSKF Model: Time Series Prediction with Spatial Adjustments.- 14.3.1 Multi-State Kaiman Filter.- 14.3.2 Spatial Adjustment via Hierarchical Random Effects Model.- 14.3.3 CIHM Method.- 14.3.4 C-MSKF.- 14.4 Case Study and Observational Setting.- 14.4.1 Data.- 14.4.2 Treatments.- 14.5 Results.- >14.6 Conclusions.- >References.- Appendix 1: Poolbayes Program.- 15 A Multiprocess Mixture Model to Estimate Space-Time Dimensions of Weekly Pricing of Certificates of Deposit.- 15.1 Introduction.- 15.2 A Dynamic Targeting Model of CD Rate-Setting Behavior.- 15.2.1 The Model.- 15.2.2 The Decision Rule.- 15.3 The Spatial Econometric Model.- 15.3.1 Spatial Time-Varying Parameters.- 15.3.2 Parameter Estimation.- 15.3.3 Testing Hypotheses with the Model.- 15.4 Implementing the Model.- 15.4.1 The Data.- 15.4.2 Prior Information.- 15.4.3 Empirical Results.- 15.5 Conclusions.- Acknowledgements.- References.- Appendix 1: FORTRAN Program for the Spatial Mixture.- Author Index.- Contributors.