Bayesian Inference in the Social Sciences 1st Edition by Ivan Jeliazkov ISBN 978-1118771211 1118771214

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Product details: 

ISBN 10:  1118771214

ISBN 13: 978-1118771211

Author:  Ivan Jeliazkov

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.

Table of contents: 

1. Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics – Zack W. Almquist and Carter T. Butts

2. Ethnic Minority Rule and Civil War: A Bayesian Dynamic Multilevel Analysis – Xun Pang

3. Bayesian Analysis of Treatment Effect Models – Mingliang Li and Justin L. Tobias

4. Bayesian Analysis of Sample Selection Models – Martijn van Hasselt

5. Modern Bayesian Factor Analysis – Hedibert Freitas Lopes

6. Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence – Joshua C.C. Chan and Cody Y.L. Hsiao

7. From the Great Depression to the Great Recession: A Model-based Ranking of U.S. Recessions – Rui Liu and Ivan Jeliazkov

8. What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models – Paskalis Glabadanidis

9. Stochastic Search for Price Insensitive Consumers – Eric Eisenstat

10. Hierarchical Modeling of Choice Concentration of U.S. Households – Karsten T. Hansen, Romana Khan, and Vishal Singh

11. Approximate Bayesian Inference in Models Defined through Estimating Equations

12. Reacting to Surprising Seemingly Inappropriate Results – Dale J. Poirier

13. Identification and MCMC Estimation of Bivariate Probit Models with Partial Observability – Ashish Rajbhandari

14. School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach – Kazuhiko Kakamu and Hajime Wago

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Tags: Ivan Jeliazkov, Bayesian Inference, the Social Sciences