Bayesian Inference in the Social Sciences
-15%
portes grátis
Bayesian Inference in the Social Sciences
Yang, Xin-She; Jeliazkov, Ivan
John Wiley & Sons Inc
10/2014
352
Dura
Inglês
9781118771211
15 a 20 dias
610
Descrição não disponível.
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
Joshua C.C. Chan and Cody Y.L. Hsiao
6.1 Introduction 159
6.2 Stochastic Volatility Model 160
6.3 Moving Average Stochastic Volatility Model 168
6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173
References 178
7 From the Great Depression to the Great Recession: A Modelbased Ranking of U.S. Recessions 181
Rui Liu and Ivan Jeliazkov
7.1 Introduction 181
7.2 Methodology 183
7.3 Results 188
7.4 Conclusions 191
Appendix: Data 192
References 192
8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 201
Paskalis Glabadanidis
8.1 Introduction 202
8.2 Methodology 204
8.3 Data 205
8.4 Empirical Results 206
8.5 Concluding Remarks 212
9 Stochastic Search For Price Insensitive Consumers 227
Eric Eisenstat
9.1 Introduction 228
9.2 Random utility models in marketing applications 230
9.3 The censored mixing distribution in detail 234
9.4 Reference price models with price thresholds 240
9.5 Conclusion 244
References 245
10 Hierarchical Modeling of Choice Concentration of US Households 249
Karsten T. Hansen, Romana Khan and Vishal Singh
10.1 Introduction 250
10.2 Data Description 252
10.3 Measures of Choice Concentration 252
10.4 Methodology 254
10.5 Results 256
10.6 Interpreting ? 260
10.7 Decomposing the effects of time, number of decisions and concentration preference 263
10.8 Conclusion 265
References 267
11 Approximate Bayesian inference in models defined through estimating equations 269
11.1 Introduction 269
11.2 Examples 271
11.3 Frequentist estimation 273
11.4 Bayesian estimation 276
11.5 Simulating from the posteriors 281
11.6 Asymptotic theory 283
11.7 Bayesian validity 285
11.8 Application 286
11.9 Conclusions 288
12 Reacting to Surprising Seemingly Inappropriate Results 295
Dale J. Poirier
12.1 Introduction 295
12.2 Statistical Framework 296
12.3 Empirical Illustration 300
12.4 Discussion 301
References 301
13 Identification and MCMC estimation of bivariate probit models with partial observability 303
Ashish Rajbhandari
13.1 Introduction 303
13.2 Bivariate Probit Model 305
13.3 Identification in a partially observable model 307
13.4 Monte Carlo Simulations 308
13.5 Bayesian Methodology 309
13.6 Application 312
13.7 Conclusion 315
Chapter Appendix 316
References 317
14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach 321
Kazuhiko Kakamu and Hajime Wago
14.1 Introduction 321
14.2 The Model 323
14.3 Posterior Analysis 325
14.4 Empirical Analysis 326
14.5 Conclusions 330
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
Joshua C.C. Chan and Cody Y.L. Hsiao
6.1 Introduction 159
6.2 Stochastic Volatility Model 160
6.3 Moving Average Stochastic Volatility Model 168
6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173
References 178
7 From the Great Depression to the Great Recession: A Modelbased Ranking of U.S. Recessions 181
Rui Liu and Ivan Jeliazkov
7.1 Introduction 181
7.2 Methodology 183
7.3 Results 188
7.4 Conclusions 191
Appendix: Data 192
References 192
8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 201
Paskalis Glabadanidis
8.1 Introduction 202
8.2 Methodology 204
8.3 Data 205
8.4 Empirical Results 206
8.5 Concluding Remarks 212
9 Stochastic Search For Price Insensitive Consumers 227
Eric Eisenstat
9.1 Introduction 228
9.2 Random utility models in marketing applications 230
9.3 The censored mixing distribution in detail 234
9.4 Reference price models with price thresholds 240
9.5 Conclusion 244
References 245
10 Hierarchical Modeling of Choice Concentration of US Households 249
Karsten T. Hansen, Romana Khan and Vishal Singh
10.1 Introduction 250
10.2 Data Description 252
10.3 Measures of Choice Concentration 252
10.4 Methodology 254
10.5 Results 256
10.6 Interpreting ? 260
10.7 Decomposing the effects of time, number of decisions and concentration preference 263
10.8 Conclusion 265
References 267
11 Approximate Bayesian inference in models defined through estimating equations 269
11.1 Introduction 269
11.2 Examples 271
11.3 Frequentist estimation 273
11.4 Bayesian estimation 276
11.5 Simulating from the posteriors 281
11.6 Asymptotic theory 283
11.7 Bayesian validity 285
11.8 Application 286
11.9 Conclusions 288
12 Reacting to Surprising Seemingly Inappropriate Results 295
Dale J. Poirier
12.1 Introduction 295
12.2 Statistical Framework 296
12.3 Empirical Illustration 300
12.4 Discussion 301
References 301
13 Identification and MCMC estimation of bivariate probit models with partial observability 303
Ashish Rajbhandari
13.1 Introduction 303
13.2 Bivariate Probit Model 305
13.3 Identification in a partially observable model 307
13.4 Monte Carlo Simulations 308
13.5 Bayesian Methodology 309
13.6 Application 312
13.7 Conclusion 315
Chapter Appendix 316
References 317
14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach 321
Kazuhiko Kakamu and Hajime Wago
14.1 Introduction 321
14.2 The Model 323
14.3 Posterior Analysis 325
14.4 Empirical Analysis 326
14.5 Conclusions 330
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
social sciences; political science; sociology; economics; marketing; finance; Bayesian model formulation; Bayesian methods; Bayesian estimation; Bayesian applications; Markov chain Monte Carlo algorithms; mathematical modeling and simulation; asset pricing under fat-tailed distributions; price indifference modeling and market segmentation; analysis of dynamic networks; ethnic minorities and civil war; school choice effects; business cycles and macroeconomic performance; numerical methods; computational analysis; time series techniques; stochastic volatility; dynamic factor; time-varying parameter models
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
Joshua C.C. Chan and Cody Y.L. Hsiao
6.1 Introduction 159
6.2 Stochastic Volatility Model 160
6.3 Moving Average Stochastic Volatility Model 168
6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173
References 178
7 From the Great Depression to the Great Recession: A Modelbased Ranking of U.S. Recessions 181
Rui Liu and Ivan Jeliazkov
7.1 Introduction 181
7.2 Methodology 183
7.3 Results 188
7.4 Conclusions 191
Appendix: Data 192
References 192
8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 201
Paskalis Glabadanidis
8.1 Introduction 202
8.2 Methodology 204
8.3 Data 205
8.4 Empirical Results 206
8.5 Concluding Remarks 212
9 Stochastic Search For Price Insensitive Consumers 227
Eric Eisenstat
9.1 Introduction 228
9.2 Random utility models in marketing applications 230
9.3 The censored mixing distribution in detail 234
9.4 Reference price models with price thresholds 240
9.5 Conclusion 244
References 245
10 Hierarchical Modeling of Choice Concentration of US Households 249
Karsten T. Hansen, Romana Khan and Vishal Singh
10.1 Introduction 250
10.2 Data Description 252
10.3 Measures of Choice Concentration 252
10.4 Methodology 254
10.5 Results 256
10.6 Interpreting ? 260
10.7 Decomposing the effects of time, number of decisions and concentration preference 263
10.8 Conclusion 265
References 267
11 Approximate Bayesian inference in models defined through estimating equations 269
11.1 Introduction 269
11.2 Examples 271
11.3 Frequentist estimation 273
11.4 Bayesian estimation 276
11.5 Simulating from the posteriors 281
11.6 Asymptotic theory 283
11.7 Bayesian validity 285
11.8 Application 286
11.9 Conclusions 288
12 Reacting to Surprising Seemingly Inappropriate Results 295
Dale J. Poirier
12.1 Introduction 295
12.2 Statistical Framework 296
12.3 Empirical Illustration 300
12.4 Discussion 301
References 301
13 Identification and MCMC estimation of bivariate probit models with partial observability 303
Ashish Rajbhandari
13.1 Introduction 303
13.2 Bivariate Probit Model 305
13.3 Identification in a partially observable model 307
13.4 Monte Carlo Simulations 308
13.5 Bayesian Methodology 309
13.6 Application 312
13.7 Conclusion 315
Chapter Appendix 316
References 317
14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach 321
Kazuhiko Kakamu and Hajime Wago
14.1 Introduction 321
14.2 The Model 323
14.3 Posterior Analysis 325
14.4 Empirical Analysis 326
14.5 Conclusions 330
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
Joshua C.C. Chan and Cody Y.L. Hsiao
6.1 Introduction 159
6.2 Stochastic Volatility Model 160
6.3 Moving Average Stochastic Volatility Model 168
6.4 Stochastic Volatility Models with Heavy-Tailed Error Distributions 173
References 178
7 From the Great Depression to the Great Recession: A Modelbased Ranking of U.S. Recessions 181
Rui Liu and Ivan Jeliazkov
7.1 Introduction 181
7.2 Methodology 183
7.3 Results 188
7.4 Conclusions 191
Appendix: Data 192
References 192
8 What Difference Fat Tails Make: A Bayesian MCMC Estimation of Empirical Asset Pricing Models 201
Paskalis Glabadanidis
8.1 Introduction 202
8.2 Methodology 204
8.3 Data 205
8.4 Empirical Results 206
8.5 Concluding Remarks 212
9 Stochastic Search For Price Insensitive Consumers 227
Eric Eisenstat
9.1 Introduction 228
9.2 Random utility models in marketing applications 230
9.3 The censored mixing distribution in detail 234
9.4 Reference price models with price thresholds 240
9.5 Conclusion 244
References 245
10 Hierarchical Modeling of Choice Concentration of US Households 249
Karsten T. Hansen, Romana Khan and Vishal Singh
10.1 Introduction 250
10.2 Data Description 252
10.3 Measures of Choice Concentration 252
10.4 Methodology 254
10.5 Results 256
10.6 Interpreting ? 260
10.7 Decomposing the effects of time, number of decisions and concentration preference 263
10.8 Conclusion 265
References 267
11 Approximate Bayesian inference in models defined through estimating equations 269
11.1 Introduction 269
11.2 Examples 271
11.3 Frequentist estimation 273
11.4 Bayesian estimation 276
11.5 Simulating from the posteriors 281
11.6 Asymptotic theory 283
11.7 Bayesian validity 285
11.8 Application 286
11.9 Conclusions 288
12 Reacting to Surprising Seemingly Inappropriate Results 295
Dale J. Poirier
12.1 Introduction 295
12.2 Statistical Framework 296
12.3 Empirical Illustration 300
12.4 Discussion 301
References 301
13 Identification and MCMC estimation of bivariate probit models with partial observability 303
Ashish Rajbhandari
13.1 Introduction 303
13.2 Bivariate Probit Model 305
13.3 Identification in a partially observable model 307
13.4 Monte Carlo Simulations 308
13.5 Bayesian Methodology 309
13.6 Application 312
13.7 Conclusion 315
Chapter Appendix 316
References 317
14 School Choice Effects in Tokyo Metropolitan Area: A Bayesian Spatial Quantile Regression Approach 321
Kazuhiko Kakamu and Hajime Wago
14.1 Introduction 321
14.2 The Model 323
14.3 Posterior Analysis 325
14.4 Empirical Analysis 326
14.5 Conclusions 330
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
social sciences; political science; sociology; economics; marketing; finance; Bayesian model formulation; Bayesian methods; Bayesian estimation; Bayesian applications; Markov chain Monte Carlo algorithms; mathematical modeling and simulation; asset pricing under fat-tailed distributions; price indifference modeling and market segmentation; analysis of dynamic networks; ethnic minorities and civil war; school choice effects; business cycles and macroeconomic performance; numerical methods; computational analysis; time series techniques; stochastic volatility; dynamic factor; time-varying parameter models
