Bayesian Inference in the Social Sciences
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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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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.
