{"product_id":"bayesian-inference-in-the-social-sciences-isbn-9781118771211","title":"Bayesian Inference in the Social Sciences","description":"\u003cp\u003e\u003cb\u003ePresents new models, methods, and techniques and considers important real-world applications in political science, sociology, economics, marketing, and finance\u003c\/b\u003e\u003cb\u003e\u003cbr\u003e \u003cbr\u003e \u003c\/b\u003eEmphasizing interdisciplinary coverage, \u003ci\u003eBayesian Inference in the Social Sciences\u003c\/i\u003e 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.\u003cbr\u003e \u003cbr\u003e \u003ci\u003eBayesian Inference in the Social Sciences\u003c\/i\u003e 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:\u003cbr\u003e \u003cbr\u003e \u003c\/p\u003e \u003cul\u003e \u003cli\u003eReal-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\u003c\/li\u003e \u003cli\u003eState-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book’s supplemental website\u003c\/li\u003e \u003cli\u003eInterdisciplinary coverage from well-known international scholars and practitioners\u003c\/li\u003e \u003c\/ul\u003e \u003ci\u003e\u003cbr\u003e Bayesian Inference in the Social Sciences\u003c\/i\u003e 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.  \u003cp\u003eList of Figures iii\u003cbr\u003e \u003cbr\u003e \u003cb\u003e1 Bayesian Analysis of Dynamic Network Regression with Joint\u003c\/b\u003e \u003cb\u003eEdge\/Vertex Dynamics 1\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eZack W. Almquist and Carter T. Butts\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e1.1 Introduction 2\u003cbr\u003e \u003cbr\u003e 1.2 Statistical Models for Social Network Data 2\u003cbr\u003e \u003cbr\u003e 1.3 Dynamic Network Logistic Regression with Vertex Dynamics 11\u003cbr\u003e \u003cbr\u003e 1.4 Empirical Examples and Simulation Analysis 14\u003cbr\u003e \u003cbr\u003e 1.5 Discussion 29\u003cbr\u003e \u003cbr\u003e 1.6 Conclusion 30\u003cbr\u003e \u003cbr\u003e \u003cb\u003e2 Ethnic Minority Rule and Civil War: A Bayesian Dynamic Multilevel\u003c\/b\u003e \u003cb\u003eAnalysis 39\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eXun Pang\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e2.1 Introduction: Ethnic Minority Rule and Civil War 40\u003cbr\u003e \u003cbr\u003e 2.2 EMR: Grievance and Opportunities of Rebellion 41\u003cbr\u003e \u003cbr\u003e 2.3 Bayesian GLMM-AR(p) Model 42\u003cbr\u003e \u003cbr\u003e 2.4 Variables, Model and Data 47\u003cbr\u003e \u003cbr\u003e 2.5 Empirical Results and Interpretation 49\u003cbr\u003e \u003cbr\u003e 2.6 Civil War: Prediction 54\u003cbr\u003e \u003cbr\u003e 2.7 Robustness Checking: Alternative Measures of EMR 59\u003cbr\u003e \u003cbr\u003e 2.8 Conclusion 60\u003cbr\u003e \u003cbr\u003e References 62\u003cbr\u003e \u003cbr\u003e \u003cb\u003e3 Bayesian Analysis of Treatment Effect Models 67\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMingliang Li and Justin L. Tobias\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e3.1 Introduction 68\u003cbr\u003e \u003cbr\u003e 3.2 Linear Treatment Response Models Under Normality 69\u003cbr\u003e \u003cbr\u003e 3.3 Nonlinear Treatment Response Models 73\u003cbr\u003e \u003cbr\u003e 3.4 Other Issues and Extensions: Non-Normality, Model Selection and Instrument Imperfection 78\u003cbr\u003e \u003cbr\u003e 3.5 Illustrative Application 84\u003cbr\u003e \u003cbr\u003e 3.6 Conclusion 89\u003cbr\u003e \u003cbr\u003e \u003cb\u003e4 Bayesian Analysis of Sample Selection Models 95\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eMartijn van Hasselt\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e4.1 Introduction 95\u003cbr\u003e \u003cbr\u003e 4.2 Univariate Selection Models 97\u003cbr\u003e \u003cbr\u003e 4.3 Multivariate Selection Models 101\u003cbr\u003e \u003cbr\u003e 4.4 Semiparametric Models 111\u003cbr\u003e \u003cbr\u003e 4.5 Conclusion 114\u003cbr\u003e \u003cbr\u003e References 114\u003cbr\u003e \u003cbr\u003e \u003cb\u003e5 Modern Bayesian Factor Analysis 117\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eHedibert Freitas Lopes\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e5.1 Introduction 117\u003cbr\u003e \u003cbr\u003e 5.2 Normal linear factor analysis 119\u003cbr\u003e \u003cbr\u003e 5.3 Factor stochastic volatility 125\u003cbr\u003e \u003cbr\u003e 5.4 Spatial factor analysis 128\u003cbr\u003e \u003cbr\u003e 5.5 Additional developments 133\u003cbr\u003e \u003cbr\u003e 5.6 Modern non-Bayesian factor analysis 136\u003cbr\u003e \u003cbr\u003e 5.7 Final remarks 137\u003cbr\u003e \u003cbr\u003e \u003cb\u003e6 Estimation of stochastic volatility models with heavy tails and\u003c\/b\u003e \u003cb\u003eserial dependence 159\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eJoshua C.C. Chan and Cody Y.L. Hsiao\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e6.1 Introduction 159\u003cbr\u003e \u003cbr\u003e 6.2 Stochastic Volatility Model 160\u003cbr\u003e \u003cbr\u003e 6.3 Moving Average Stochastic Volatility Model 168\u003cbr\u003e \u003cbr\u003e 6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173\u003cbr\u003e \u003cbr\u003e References 178\u003cbr\u003e \u003cbr\u003e \u003cb\u003e7 From the Great Depression to the Great Recession: A Modelbased\u003c\/b\u003e \u003cb\u003eRanking of U.S. Recessions 181\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eRui Liu and Ivan Jeliazkov\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e7.1 Introduction 181\u003cbr\u003e \u003cbr\u003e 7.2 Methodology 183\u003cbr\u003e \u003cbr\u003e 7.3 Results 188\u003cbr\u003e \u003cbr\u003e 7.4 Conclusions 191\u003cbr\u003e \u003cbr\u003e Appendix: Data 192\u003cbr\u003e \u003cbr\u003e References 192\u003cbr\u003e \u003cbr\u003e \u003cb\u003e8 What Difference Fat Tails Make: A Bayesian MCMC Estimation\u003c\/b\u003e \u003cb\u003eof Empirical Asset Pricing Models 201\u003cbr\u003e \u003c\/b\u003e\u003ci\u003ePaskalis Glabadanidis\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e8.1 Introduction 202\u003cbr\u003e \u003cbr\u003e 8.2 Methodology 204\u003cbr\u003e \u003cbr\u003e 8.3 Data 205\u003cbr\u003e \u003cbr\u003e 8.4 Empirical Results 206\u003cbr\u003e \u003cbr\u003e 8.5 Concluding Remarks 212\u003cbr\u003e \u003cbr\u003e \u003cb\u003e9 Stochastic Search For Price Insensitive Consumers 227\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eEric Eisenstat\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e9.1 Introduction 228\u003cbr\u003e \u003cbr\u003e 9.2 Random utility models in marketing applications 230\u003cbr\u003e \u003cbr\u003e 9.3 The censored mixing distribution in detail 234\u003cbr\u003e \u003cbr\u003e 9.4 Reference price models with price thresholds 240\u003cbr\u003e \u003cbr\u003e 9.5 Conclusion 244\u003cbr\u003e \u003cbr\u003e References 245\u003cbr\u003e \u003cbr\u003e \u003cb\u003e10 Hierarchical Modeling of Choice Concentration of US Households 249\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKarsten T. Hansen, Romana Khan and Vishal Singh\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e10.1 Introduction 250\u003cbr\u003e \u003cbr\u003e 10.2 Data Description 252\u003cbr\u003e \u003cbr\u003e 10.3 Measures of Choice Concentration 252\u003cbr\u003e \u003cbr\u003e 10.4 Methodology 254\u003cbr\u003e \u003cbr\u003e 10.5 Results 256\u003cbr\u003e \u003cbr\u003e 10.6 Interpreting \u003ci\u003eθ\u003c\/i\u003e 260\u003cbr\u003e \u003cbr\u003e 10.7 Decomposing the effects of time, number of decisions and concentration preference 263\u003cbr\u003e \u003cbr\u003e 10.8 Conclusion 265\u003cbr\u003e \u003cbr\u003e References 267\u003cbr\u003e \u003cbr\u003e \u003cb\u003e11 Approximate Bayesian inference in models defined through estimating\u003c\/b\u003e \u003cb\u003eequations 269\u003cbr\u003e \u003cbr\u003e \u003c\/b\u003e11.1 Introduction 269\u003cbr\u003e \u003cbr\u003e 11.2 Examples 271\u003cbr\u003e \u003cbr\u003e 11.3 Frequentist estimation 273\u003cbr\u003e \u003cbr\u003e 11.4 Bayesian estimation 276\u003cbr\u003e \u003cbr\u003e 11.5 Simulating from the posteriors 281\u003cbr\u003e \u003cbr\u003e 11.6 Asymptotic theory 283\u003cbr\u003e \u003cbr\u003e 11.7 Bayesian validity 285\u003cbr\u003e \u003cbr\u003e 11.8 Application 286\u003cbr\u003e \u003cbr\u003e 11.9 Conclusions 288\u003cbr\u003e \u003cbr\u003e \u003cb\u003e12 Reacting to Surprising Seemingly Inappropriate Results 295\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eDale J. Poirier\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e12.1 Introduction 295\u003cbr\u003e \u003cbr\u003e 12.2 Statistical Framework 296\u003cbr\u003e \u003cbr\u003e 12.3 Empirical Illustration 300\u003cbr\u003e \u003cbr\u003e 12.4 Discussion 301\u003cbr\u003e \u003cbr\u003e References 301\u003cbr\u003e \u003cbr\u003e \u003cb\u003e13 Identification and MCMC estimation of bivariate probit models w\u003c\/b\u003e\u003cb\u003eith partial observability 303\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eAshish Rajbhandari\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e13.1 Introduction 303\u003cbr\u003e \u003cbr\u003e 13.2 Bivariate Probit Model 305\u003cbr\u003e \u003cbr\u003e 13.3 Identification in a partially observable model 307\u003cbr\u003e \u003cbr\u003e 13.4 Monte Carlo Simulations 308\u003cbr\u003e \u003cbr\u003e 13.5 Bayesian Methodology 309\u003cbr\u003e \u003cbr\u003e 13.6 Application 312\u003cbr\u003e \u003cbr\u003e 13.7 Conclusion 315\u003cbr\u003e \u003cbr\u003e Chapter Appendix 316\u003cbr\u003e \u003cbr\u003e References 317\u003cbr\u003e \u003cbr\u003e \u003cb\u003e14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian\u003c\/b\u003e \u003cb\u003eSpatial Quantile Regression Approach 321\u003cbr\u003e \u003c\/b\u003e\u003ci\u003eKazuhiko Kakamu and Hajime Wago\u003cbr\u003e \u003cbr\u003e \u003c\/i\u003e14.1 Introduction 321\u003cbr\u003e \u003cbr\u003e 14.2 The Model 323\u003cbr\u003e \u003cbr\u003e 14.3 Posterior Analysis 325\u003cbr\u003e \u003cbr\u003e 14.4 Empirical Analysis 326\u003cbr\u003e \u003cbr\u003e 14.5 Conclusions 330\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eIVAN JELIAZKOV, PhD,\u003c\/b\u003e is Associate Professor of Economics and Statistics at the University of California, Irvine. Dr. Jeliazkov's research interests include Bayesian econometrics and discrete data analysis, model comparison, and simulation-based inference. In addition to developing new methods and estimation techniques, his work features applications in a variety of disciplines, including micro- and macroeconomics, marketing, political science, transportation, and environmental engineering.  \u003c\/p\u003e\u003cp\u003e\u003cb\u003eXIN-SHE YANG, PhD,\u003c\/b\u003e is Reader in Modeling and Optimization at Middlesex University, United Kingdom, as well as Adjunct Professor at Reykjavik University, Iceland. He is the author of \u003ci\u003eMathematical Modeling with Multidisciplinary Applications\u003c\/i\u003e and \u003ci\u003eEngineering Optimization: An Introduction with Metaheuristic Applications\u003c\/i\u003e, both of which are published by Wiley.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003ePRESENTS NEW MODELS, METHODS, AND TECHNIQUES AND CONSIDERS IMPORTANT REAL-WORLD APPLICATIONS IN POLITICAL SCIENCE, SOCIOLOGY, ECONOMICS, MARKETING, AND FINANCE\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eEmphasizing interdisciplinary coverage, \u003ci\u003eBayesian Inference in the Social Sciences\u003c\/i\u003e 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. \u003c\/p\u003e\u003cp\u003e\u003ci\u003eBayesian Inference in the Social Sciences\u003c\/i\u003e 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: \u003c\/p\u003e\u003cul\u003e \u003cli\u003eReal-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\u003c\/li\u003e \u003cli\u003eState-of-the-art computational tools and Markov chain Monte Carlo algorithms with related materials available via the book's supplemental website\u003c\/li\u003e \u003cli\u003eInterdisciplinary coverage from well-known international scholars and practitioners\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eBayesian Inference in the Social Sciences\u003c\/i\u003e 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.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988793868517,"sku":"NP9781118771211","price":144.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118771211.jpg?v=1761781613","url":"https:\/\/k12savings.com\/products\/bayesian-inference-in-the-social-sciences-isbn-9781118771211","provider":"K12savings","version":"1.0","type":"link"}