Bayesian Inference in the Social Sciences (eBook, ePUB)

Redaktion: Jeliazkov, Ivan; Yang, Xin-She

• Format: ePub

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 352
• Erscheinungstermin: 4. November 2014
• Englisch
• ISBN-13: 9781118771129
• Artikelnr.: 41821763

Autorenporträt

IVAN JELIAZKOV, PhD, 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. XIN-SHE YANG, PhD, 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 Mathematical Modeling with Multidisciplinary Applications and Engineering Optimization: An Introduction with Metaheuristic Applications, both of which are published by Wiley.

Inhaltsangabe

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 theta 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