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.
关于作者
IVAN JELIAZKOV, Ph D, 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, Ph D, 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.
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