- List of Figures iii
- 1 Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics 1 Zack W. Almquist and Carter T. Butts
- 1.1 Introduction 2
- 1.2 Statistical Models for Social Network Data 2
- 1.3 Dynamic Network Logistic Regression with Vertex Dynamics 11
- 1.4 Empirical Examples and Simulation Analysis 14
- 1.5 Discussion 29
- 1.6 Conclusion 30
- 2 Ethnic Minority Rule and Civil War: A Bayesian Dynamic Multilevel Analysis 39 Xun Pang
- 2.1 Introduction: Ethnic Minority Rule and Civil War 40
- 2.2 EMR: Grievance and Opportunities of Rebellion 41
- 2.3 Bayesian GLMM-AR(p) Model 42
- 2.4 Variables, Model and Data 47
- 2.5 Empirical Results and Interpretation 49
- 2.6 Civil War: Prediction 54
- 2.7 Robustness Checking: Alternative Measures of EMR 59
- 2.8 Conclusion 60
- References 62
- 3 Bayesian Analysis of Treatment Effect Models 67 Mingliang Li and Justin L. Tobias
- 3.1 Introduction 68
- 3.2 Linear Treatment Response Models Under Normality 69
- 3.3 Nonlinear Treatment Response Models 73
- 3.4 Other Issues and Extensions: Non-Normality, Model Selection and Instrument Imperfection 78
- 3.5 Illustrative Application 84
- 3.6 Conclusion 89
- 4 Bayesian Analysis of Sample Selection Models 95 Martijn van Hasselt
- 4.1 Introduction 95
- 4.2 Univariate Selection Models 97
- 4.3 Multivariate Selection Models 101
- 4.4 Semiparametric Models 111
- 4.5 Conclusion 114
- References 114
- 5 Modern Bayesian Factor Analysis 117 Hedibert Freitas Lopes
- 5.1 Introduction 117
- 5.2 Normal linear factor analysis 119
- 5.3 Factor stochastic volatility 125
- 5.4 Spatial factor analysis 128
- 5.5 Additional developments 133
- 5.6 Modern non-Bayesian factor analysis 136
- 5.7 Final remarks 137
- 6 Estimation of stochastic volatility models with heavy tails and serial dependence 159 Joshua C.C. Chan and Cody Y.L. Hsiao
- 6.1 Introduction 159
- 6.2 Stochastic Volatility Model 160
- 6.3 Moving Average Stochastic Volatility Model 168
- 6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173
- References 178
- 7 From the Great Depression to the Great Recession: A Modelbased Ranking of U.S. Recessions 181 Rui Liu and Ivan Jeliazkov
- 7.1 Introduction 181
- 7.2 Methodology 183
- 7.3 Results 188
- 7.4 Conclusions 191
- Appendix: Data 192
- References 192
- 8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 201 Paskalis Glabadanidis
- 8.1 Introduction 202
- 8.2 Methodology 204
- 8.3 Data 205
- 8.4 Empirical Results 206
- 8.5 Concluding Remarks 212
- 9 Stochastic Search For Price Insensitive Consumers 227 Eric Eisenstat
- 9.1 Introduction 228
- 9.2 Random utility models in marketing applications 230
- 9.3 The censored mixing distribution in detail 234
- 9.4 Reference price models with price thresholds 240
- 9.5 Conclusion 244
- References 245
- 10 Hierarchical Modeling of Choice Concentration of US Households 249 Karsten T. Hansen, Romana Khan and Vishal Singh
- 10.1 Introduction 250
- 10.2 Data Description 252
- 10.3 Measures of Choice Concentration 252
- 10.4 Methodology 254
- 10.5 Results 256
- 10.6 Interpreting 260
- 10.7 Decomposing the effects of time, number of decisions and concentration preference 263
- 10.8 Conclusion 265
- References 267
- 11 Approximate Bayesian inference in models defined through estimating equations 269
- 11.1 Introduction 269
- 11.2 Examples 271
- 11.3 Frequentist estimation 273
- 11.4 Bayesian estimation 276
- 11.5 Simulating from the posteriors 281
- 11.6 Asymptotic theory 283
- 11.7 Bayesian validity 285
- 11.8 Application 286
- 11.9 Conclusions 288
- 12 Reacting to Surprising Seemingly Inappropriate Results 295 Dale J. Poirier
- 12.1 Introduction 295
- 12.2 Statistical Framework 296
- 12.3 Empirical Illustration 300
- 12.4 Discussion 301
- References 301
- 13 Identification and MCMC estimation of bivariate probit models with partial observability 303 Ashish Rajbhandari
- 13.1 Introduction 303
- 13.2 Bivariate Probit Model 305
- 13.3 Identification in a partially observable model 307
- 13.4 Monte Carlo Simulations 308
- 13.5 Bayesian Methodology 309
- 13.6 Application 312
- 13.7 Conclusion 315
- Chapter Appendix 316
- References 317
- 14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach 321 Kazuhiko Kakamu and Hajime Wago
- 14.1 Introduction 321
- 14.2 The Model 323
- 14.3 Posterior Analysis 325
- 14.4 Empirical Analysis 326
- 14.5 Conclusions 330.
- (source: Nielsen Book Data)

Presents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance Emphasizing interdisciplinary coverage, Bayesian Inference in the Social Sciences builds upon the recent growth in Bayesian methodology and examines an array of topics in model formulation, estimation, and applications. The book presents recent and trending developments in a diverse, yet closely integrated, set of research topics within the social sciences and facilitates the transmission of new ideas and methodology across disciplines while maintaining manageability, coherence, and a clear focus. Bayesian Inference in the Social Sciences features innovative methodology and novel applications in addition to new theoretical developments and modeling approaches, including the formulation and analysis of models with partial observability, sample selection, and incomplete data. Additional areas of inquiry include a Bayesian derivation of empirical likelihood and method of moment estimators, and the analysis of treatment effect models with endogeneity. The book emphasizes practical implementation, reviews and extends estimation algorithms, and examines innovative applications in a multitude of fields. Time series techniques and algorithms are discussed for stochastic volatility, dynamic factor, and time-varying parameter models. Additional features include: Real-world applications and case studies that highlight asset pricing under fat-tailed distributions, price indifference modeling and market segmentation, analysis of dynamic networks, ethnic minorities and civil war, school choice effects, and business cycles and macroeconomic performance State-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book s supplemental website Interdisciplinary coverage from well-known international scholars and practitioners Bayesian Inference in the Social Sciences is an ideal reference for researchers in economics, political science, sociology, and business as well as an excellent resource for academic, government, and regulation agencies. The book is also useful for graduate-level courses in applied econometrics, statistics, mathematical modeling and simulation, numerical methods, computational analysis, and the social sciences.

(source: Nielsen Book Data)