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Bayesian Inference in the Social Sciences PDF

352 Pages·2014·16.431 MB·English
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Bayesian Inference in the Social Sciences Bayesian Inference in the Social Sciences Edited by Ivan Jeliazkov Department of Economics University of California, Irvine California, USA Xin-She Yang School of Science and Technology Middlesex University London, UK WILEY Copyright © 2014 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, (978) 750-8400, fax (978) 750-4470, or on the web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, (201) 748-6011, fax (201) 748-6008, or online at http://www.wiley.com/go/permission. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. For general information on our other products and services or for technical support, please contact our Customer Care Department within the United States at (800) 762-2974, outside the United States at (317) 572-3993 or fax (317) 572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic format. For information about Wiley products, visit our web site at www.wiley.com. Library of Congress Cataloging-in-Publication Data: Bayesian inference in the social sciences / edited by Ivan Jeliazkov, Department of Economics, University of California, Irvine, California, USA, Xin-She Yang, School of Science and Technology, Middlesex University, London, United Kingdom, pages cm Includes bibliographical references and index. ISBN 978-1-118-77121-1 (hardback) 1. Social sciences—Statistical methods. 2. Bayesian statistical decision theory. I. Jeliazkov, Ivan, 1973- II. Yang, Xin-She. HA29.B38345 2014 519.5'42—dc23 2014011437 Printed in the United States of America. 10 9 8 7 6 5 4 3 21 CONTENTS Preface xiii 1 Bayesian Analysis of Dynamic Network Regression with Joint Edge/Vertex Dynamics 1 Zack W. Almquist and Carter T. Butts 1.1 Introduction 1 1.2 Statistical Models for Social Network Data 2 1.2.1 Network Data and Nomenclature 2 1.2.2 Exponential Family Random Graph Models 3 1.2.3 Temporal Models for Network Data 7 1.3 Dynamic Network Logistic Regression with Vertex Dynamics 11 1.3.1 Bayesian Inference for DNR Parameters 11 1.3.2 Bayesian Estimation of DNR with Vertex Dynamics 13 1.4 Empirical Examples and Simulation Analysis 14 1.4.1 Blog Data 14 1.4.2 Beach Data 14 1.4.3 Case Analysis: Static Vertex Set 15 1.4.4 Bayesian DNR with Vertex Dynamics 19 v X vi CONTENTS 1.5 Discussion 25 1.6 Conclusion 26 Bibliography 27 2 Ethnic Minority Rule and Civil War: A Bayesian Dynamic Multilevel Analysis 35 Xun Pang 2.1 Introduction: Ethnic Minority Rule and Civil War 36 2.2 EMR: Grievance and Opportunities of Rebellion 37 2.3 Bayesian GLMM-AR(p) Model 38 2.3.1 General Model Specification 39 2.3.2 Parameter Estimation 40 2.3.3 Bayesian Model Comparison 42 2.4 Variables, Model, and Data 43 2.5 Empirical Results and Interpretation 46 2.6 Civil War: Prediction 51 2.6.1 Predictive Probabilities of Civil War 51 2.6.2 Receiver-Operating Characteristic Curve 53 2.7 Robustness Checking: Alternative Measures of EMR 56 2.8 Conclusion 58 Bibliography 59 3 Bayesian Analysis of Treatment Effect Models 63 Mingliang Li and Justin L. Tobias 3.1 Introduction 64 3.2 Linear Treatment Response Models Under Normality 65 3.2.1 Instruments and Identification 66 3.3 Nonlinear Treatment Response Models 69 3.3.1 A General Nonlinear Representation 70 3.4 Other Issues and Extensions: Non-Normality, Model Selection, and Instrument Imperfection 74 3.4.1 Non-Normality 74 3.4.2 Model Comparison 76 3.4.3 Instrument Imperfection 79 3.5 Illustrative Application 81 3.6 Conclusion 85 Bibliography 86 4 Bayesian Analysis of Sample Selection Models 91 CONTENTS vii Martijn van Hasselt 4.1 Introduction 91 4.2 Univariate Selection Models 93 4.2.1 General Framework 93 4.2.2 Likelihoods 94 4.2.3 Bayesian Inference 95 4.3 Multivariate Selection Models 97 4.3.1 Motivation 97 4.3.2 Heckman's Selection Model 98 4.3.3 Heckman's Selection Model: Bayesian Inference 99 4.3.4 A Model with Tobit Selection 103 4.3.5 Alternative Specifications 105 4.3.6 Endogeneity 107 4.4 Semiparametric Models 107 4.5 Conclusion 110 Bibliography 111 5 Modern Bayesian Factor Analysis 115 Hedibert Freitas Lopes 5.1 Introduction 115 5.2 Normal Linear Factor Analysis 117 5.2.1 Parsimony 118 5.2.2 Identifiability 118 5.2.3 Invariance 120 5.2.4 Posterior Inference 121 5.2.5 Number of Factors 123 5.3 Factor Stochastic Volatility 125 5.3.1 Factor Stochastic Volatility 126 5.3.2 Financial Index Models 127 5.4 Spatial Factor Analysis 129 5.4.1 Spatially Hierarchical Factor Analysis 130 5.4.2 Spatial Dynamic Factor Analysis 132 5.5 Additional Developments 134 5.5.1 Prior and Posterior Robustness 134 5.5.2 Mixture of Factor Analyzers 134 5.5.3 Factor Analysis in Time Series Modeling 136 5.5.4 Factor Analysis in Macroeconometrics 136 5.5.5 Term Structure Models 137 viii CONTENTS 5.5.6 Sparse Factor Structures 138 5.6 Modern non-Bayesian factor analysis 139 5.7 Final Remarks 139 Bibliography 141 6 Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence 155 Joshua C.C. Chan and Cody Y.L. Hsiao 6.1 Introduction 155 6.2 Stochastic Volatility Model 156 6.2.1 Auxiliary Mixture Sampler 158 6.2.2 Precision Sampler for Linear Gaussian State Space Models 159 6.2.3 Empirical Example: Modeling AUD/USD Returns 163 6.3 Moving Average Stochastic Volatility Model 164 6.3.1 Estimation 165 6.3.2 Empirical Example: Modeling PHP/USD Returns During Crisis 167 6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 169 6.4.1 Estimation 170 6.4.2 Empirical Example: Modeling Daily Returns on the Silver Spot Price 172 Bibliography 175 7 From the Great Depression to the Great Recession: A Model- Based Ranking of U.S. Recessions 177 Rui Liu and Ivan Jeliazkov 7.1 Introduction 177 7.2 Methodology 180 7.2.1 Model 180 7.2.2 Estimation Framework 181 7.3 Results 184 7.4 Conclusions 194 Appendix: Data 194 Bibliography 196 8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 199

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