Spatial econometrics : qualitative and limited dependent variables

Front Cover -- Spatial Econometrics: Qualitative and Limited Dependent Variables -- Copyright Page -- Contents -- List of Contributors -- Part I: Introduction -- Progress in Spatial Modeling of Discrete and Continuous Dependent Variables -- Part II: Discrete Dependent Variables - Maximum Likelihood -- Fast Simulated Maximum Likelihood Estimation of the Spatial Probit Model Capable of Handling Large Samples -- 1. Introduction -- 2. Spatial Probit Models -- 3. GHK Mechanics -- 3.1. GHK Applied to the Variance-Covariance Matrix -- 3.2. Sparsity and the GHK -- 3.3. GHK Applied to the Precision Matrix -- 4. GHK Performance -- 5. Empirical Examples -- 5.1. A Development Application -- 5.2. A Mortgage Application -- 6. Implementation Issues and Opportunities -- 6.1. Implementation -- 6.2. Opportunities -- 6.3. Evaluation of Likelihood -- 7. Conclusion -- Notes -- Acknowledgments -- References -- Likelihood Evaluation of High-Dimensional Spatial Latent Gaussian Models with Non-Gaussian Response Variables -- 1. Introduction -- 2. Spatial-dependent Variable Models -- 2.1. Baseline Model -- 2.2. Spatial Probit Models -- 2.3. Spatial Count Data Models -- 2.4. Censored Data -- 3. Spatial EIS -- 3.1. EIS Principle -- 3.2. Sequential EIS -- 3.3. Sequential EIS for Spatial Models -- 3.3.1. The Integrating Factors χi(u(i+1) -- ai) -- 3.3.2. Kernel Representation of f(ui&amp -- smid -- u(i+1)) -- 3.3.3. Construction of the EIS Kernel ki(u(i) -- ai) -- 3.3.4. Spatial EIS Implementation -- 3.3.5. Pseudo-Code -- 3.3.6. Sparse EIS Operations -- 3.4. Spatial Probit Model -- 3.5. Spatial Poisson Model -- 3.6. Censored Data -- 4. Monte Carlo Study -- 4.1. Spatial Probit Models -- 4.2. Spatial Poisson Models -- 5. Empirical Applications -- 5.1. Spatial Probit for the 1996 US Presidential Election -- 5.2. Spatial Count Model for US Firms Location Choices -- 6. Conclusions.