Cover -- Title Page -- Copyright Page -- CONTENTS -- Preface -- 1 Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics -- 1.1 Introduction -- 1.2 Statistical Models for Social Network Data -- 1.2.1 Network Data and Nomenclature -- 1.2.2 Exponential Family Random Graph Models -- 1.2.3 Temporal Models for Network Data -- 1.3 Dynamic Network Logistic Regression with Vertex Dynamics -- 1.3.1 Bayesian Inference for DNR Parameters -- 1.3.2 Bayesian Estimation of DNR with Vertex Dynamics -- 1.4 Empirical Examples and Simulation Analysis -- 1.4.1 Blog Data -- 1.4.2 Beach Data -- 1.4.3 Case Analysis: Static Vertex Set -- 1.4.4 Bayesian DNR with Vertex Dynamics -- 1.5 Discussion -- 1.6 Conclusion -- Bibliography -- 2 Ethnic Minority Rule and Civil War: A Bayesian Dynamic Multilevel Analysis -- 2.1 Introduction: Ethnic Minority Rule and Civil War -- 2.2 EMR: Grievance and Opportunities of Rebellion -- 2.3 Bayesian GLMM-AR(p) Model -- 2.3.1 General Model Specification -- 2.3.2 Parameter Estimation -- 2.3.3 Bayesian Model Comparison -- 2.4 Variables, Model, and Data -- 2.5 Empirical Results and Interpretation -- 2.6 Civil War: Prediction -- 2.6.1 Predictive Probabilities of Civil War -- 2.6.2 Receiver-Operating Characteristic Curve -- 2.7 Robustness Checking: Alternative Measures of EMR -- 2.8 Conclusion -- Bibliography -- 3 Bayesian Analysis of Treatment Effect Models -- 3.1 Introduction -- 3.2 Linear Treatment Response Models Under Normality -- 3.2.1 Instruments and Identification -- 3.3 Nonlinear Treatment Response Models -- 3.3.1 A General Nonlinear Representation -- 3.4 Other Issues and Extensions: Non-Normality, Model Selection, and Instrument Imperfection -- 3.4.1 Non-Normality -- 3.4.2 Model Comparison -- 3.4.3 Instrument Imperfection -- 3.5 Illustrative Application -- 3.6 Conclusion -- Bibliography.

4 Bayesian Analysis of Sample Selection Models -- 4.1 Introduction -- 4.2 Univariate Selection Models -- 4.2.1 General Framework -- 4.2.2 Likelihoods -- 4.2.3 Bayesian Inference -- 4.3 Multivariate Selection Models -- 4.3.1 Motivation -- 4.3.2 Heckman's Selection Model -- 4.3.3 Heckman's Selection Model: Bayesian Inference -- 4.3.4 A Model with Tobit Selection -- 4.3.5 Alternative Specifications -- 4.3.6 Endogeneity -- 4.4 Semiparametric Models -- 4.5 Conclusion -- Bibliography -- 5 Modern Bayesian Factor Analysis -- 5.1 Introduction -- 5.2 Normal Linear Factor Analysis -- 5.2.1 Parsimony -- 5.2.2 Identifiability -- 5.2.3 Invariance -- 5.2.4 Posterior Inference -- 5.2.5 Number of Factors -- 5.3 Factor Stochastic Volatility -- 5.3.1 Factor Stochastic Volatility -- 5.3.2 Financial Index Models -- 5.4 Spatial Factor Analysis -- 5.4.1 Spatially Hierarchical Factor Analysis -- 5.4.2 Spatial Dynamic Factor Analysis -- 5.5 Additional Developments -- 5.5.1 Prior and Posterior Robustness -- 5.5.2 Mixture of Factor Analyzers -- 5.5.3 Factor Analysis in Time Series Modeling -- 5.5.4 Factor Analysis in Macroeconometrics -- 5.5.5 Term Structure Models -- 5.5.6 Sparse Factor Structures -- 5.6 Modern non-Bayesian factor analysis -- 5.7 Final Remarks -- Bibliography -- 6 Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence -- 6.1 Introduction -- 6.2 Stochastic Volatility Model -- 6.2.1 Auxiliary Mixture Sampler -- 6.2.2 Precision Sampler for Linear Gaussian State Space Models -- 6.2.3 Empirical Example: Modeling AUD/USD Returns -- 6.3 Moving Average Stochastic Volatility Model -- 6.3.1 Estimation -- 6.3.2 Empirical Example: Modeling PHP/USD Returns During Crisis -- 6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions -- 6.4.1 Estimation -- 6.4.2 Empirical Example: Modeling Daily Returns on the Silver Spot Price.

Bibliography -- 7 From the Great Depression to the Great Recession: A Model- Based Ranking of U.S. Recessions -- 7.1 Introduction -- 7.2 Methodology -- 7.2.1 Model -- 7.2.2 Estimation Framework -- 7.3 Results -- 7.4 Conclusions -- Appendix: Data -- Bibliography -- 8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models -- 8.1 Introduction -- 8.2 Methodology -- 8.3 Data -- 8.4 Empirical Results -- 8.5 Concluding Remarks -- Bibliography -- 9 Stochastic Search For Price Insensitive Consumers -- 9.1 Introduction -- 9.2 Random Utility Models in Marketing Applications -- 9.3 The Censored Mixing Distribution in Detail -- 9.4 Reference Price Models with Price Thresholds -- 9.5 Conclusion -- Bibliography -- 10 Hierarchical Modeling of Choice Concentration of U.S. Households -- 10.1 Introduction -- 10.2 Data Description -- 10.3 Measures of Choice Concentration -- 10.4 Methodology -- 10.4.1 Specification -- 10.5 Results -- 10.5.1 Explaining Variation in Category Effects -- 10.5.2 Explaining Variation in θ -- 10.6 Interpreting θ -- 10.7 Decomposing the Effects of Time, Number of Decisions and Concentration Preference -- 10.8 Conclusion -- Bibliography -- 11 Approximate Bayesian Inference in Models Defined Through Estimating Equations -- 11.1 Introduction -- 11.2 Examples -- 11.3 Frequentist Estimation -- 11.4 Bayesian Estimation -- 11.4.1 Bayesian Bootstrap -- 11.4.2 GMM-Based Likelihoods -- 11.4.3 Empirical Likelihood-Type Posteriors -- 11.5 Simulating from the Posteriors -- 11.6 Asymptotic Theory -- 11.7 Bayesian Validity -- 11.8 Application -- 11.9 Conclusions -- Bibliography -- 12 Reacting to Surprising Seemingly Inappropriate Results -- 12.1 Introduction -- 12.2 Statistical Framework -- 12.3 Empirical Illustration -- 12.4 Discussion -- Bibliography.

13 Identification and MCMC Estimation of Bivariate Probit Models with Partial Observability -- 13.1 Introduction -- 13.2 Bivariate Probit Model -- 13.2.1 Levels of Observability -- 13.2.2 Partial Observability -- 13.3 Identification in a Partially Observable Model -- 13.4 Monte Carlo Simulations -- 13.5 Bayesian Methodology -- 13.5.1 Conditional Posteriors -- 13.6 Application -- 13.7 Conclusion -- Appendix -- Bibliography -- 14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach -- 14.1 Introduction -- 14.2 The Model -- 14.3 Posterior Analysis -- 14.3.1 The Joint Posterior Distribution -- 14.3.2 Pull Conditional Distributions for the Model -- 14.4 Empirical Analysis -- 14.5 Conclusions -- Bibliography -- Index -- EULA.