Bayesian Inference in the Social Sciences

1. Auflage 2014
ISBN: 978-1-118-77121-1
Verlag: Wiley

Buch, Englisch, 352 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 695 g

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.

Autoren/Hrsg.

Jeliazkov, Ivan

Yang, Xin-She

Fachgebiete

• Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz

Inhaltsverzeichnis

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

Über Autor(innen)

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.