Modeling ordered choices: a primer
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| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge [u.a.]
Cambridge Univ. Press
2010
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| Edition: | 1. publ. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. [337] - 360 ; Index |
| Physical Description: | XV, 365 S. graph. Darst. 26 cm |
| ISBN: | 9780521194204 0521194202 9780521142373 0521142377 |
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Record in the Search Index
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| adam_text | Titel: Modeling ordered choices
Autor: Greene, William H.
Jahr: 2010
Contents
List of tables page ix
List of figures xi
Preface xiii
Introduction: random utility and ordered choice models 1
Modeling binary choices 9
2.1 Random utility formulation of a model for binary choice 10
2.2 Probability models for binary choices 11
2.3 Estimation and inference 16
2.4 Covariance matrix estimation 26
2.5 Application of the binary choice model to health satisfaction 28
2.6 Partial effects in a binary choice model 30
2.7 Hypothesis testing 39
2.8 Goodness of fit measures 44
2.9 Heteroscedasticity 54
2.10 Panel data 57
2.11 Parameter heterogeneity 75
2.12 Endogeneity of a right-hand-side variable 80
2.13 Bivariate binary choice models 83
2.14 The multivariate probit and panel probit models 93
2.15 Endogenous sampling and case control studies 96
A model for ordered choices 99
3.1 A latent regression model for a continuous measure 99
3.2 Ordered choice as an outcome of utility maximization 103
3.3 An observed discrete outcome 105
3.4 Probabilities and the log likelihood 108
Contents
3.5 Log likelihood function 108
3.6 Analysis of data on ordered choices 109
Antecedents and contemporary counterparts 111
4.1 The origin of probit analysis: Bliss (1934a), Finney (1947a) 111
4.2 Social science data and regression analysis for binary outcomes 116
4.3 Analysis of binary choice 117
4.4 Ordered outcomes: Aitchison and Silvey (1957), Snell (1964) 117
4.5 Minimum chi squared estimation of an ordered
response model: Gurland et al. (1960) 123
4.6 Individual data and polychotomous outcomes: Walker
and Duncan (1967) 125
4.7 McKelvey and Zavoina (1975) 127
4.8 Cumulative odds model 127
4.9 Continuation ratio model 130
4.10 The ordered regression model 130
4.11 Other related models 132
4.12 The latent continuous variable 134
Estimation, inference and analysis using the ordered choice model 136
5.1 Application of the ordered choice model to self-assessed
health status 136
5.2 Distributional assumptions 138
5.3 The estimated ordered probit (logit) model 138
5.4 The estimated threshold parameters 140
5.5 Interpretation of the model - partial effects and
scaled coefficients 142
5.6 Inference 150
5.7 Prediction - computing probabilities 157
5.8 Measuring fit 160
5.9 Estimation issues 167
Specification issues and generalized models 181
6.1 Functional form issues and the generalized ordered
choice model (1) 181
6.2 Model implications for partial effects 193
6.3 Methodological issues 198
6.4 Specification tests for ordered choice models 198
vii Contents
7 Accommodating individual heterogeneity 208
7.1 Threshold models - the generalized ordered probit model (2) 209
7.2 Nonlinear specifications - a hierarchical ordered
probit (HOPIT) model 214
7.3 Thresholds and heterogeneity - anchoring vignettes 219
7.4 Heterogeneous scaling (heteroscedasticity) of random utility 232
7.5 Individually heterogeneous marginal utilities 237
Appendix: Equivalence of the vignette and HOPIT models 237
8 Parameter variation and a generalized model 239
8.1 Random-parameters models 239
8.2 Latent class and finite mixture modeling 247
8.3 Generalized ordered choice model with random thresholds (3) 262
9 Ordered choice modeling with panel and time series data 268
9.1 Ordered choice models with fixed effects 268
9.2 Ordered choice models with random effects 275
9.3 Testing for random or fixed effects: a variable addition test 278
9.4 Extending parameter heterogeneity models to ordered choices 281
9.5 Dynamic models 285
9.6 Spatial autocorrelation 289
10 Bivariate and multivariate ordered choice models 290
10.1 Multiple equations 290
10.2 Bivariate ordered probit models 291
10.3 Polychoric correlation 294
10.4 Semi-ordered bivariate probit model 295
10.5 Applications of the bivariate ordered probit model 295
10.6 A panel data version of the bivariate ordered probit model 297
10.7 Trivariate and multivariate ordered probit models 299
11 Two-part and sample selection models 302
11.1 Inflation models 302
11.2 Sample selection models 306
11.3 An ordered probit model with endogenous treatment effects 319
12 Semiparametric and nonparametric estimators and analyses 320
12.1 Heteroscedasticity 3 21
12.2 A distribution free estimator with unknown heteroscedasticitv 323
Contents
12.3 A semi-nonparametric approach 324
12.4 A partially linear model 327
12.5 Semiparametric analysis 327
12.6 A nonparametric duration model 329
References 337
Index 361
Tables
2.1 Data used in binary choice application page 29
2.2 Estimated probit and logit models 29
2.3 Alternative estimated standard errors for the probit model 30
2.4 Partial effects for probit and logit models at means of x 32
2.5 Marginal effects and average partial effects 37
2.6 Hypothesis tests 43
2.7 Homogeneity test 43
2.8 Fit measures for probit model 49
2.9a Prediction success for probit model based on y; = 1[F; .5] 50
2.9b Predictions for probit model based on probabilities 50
2.10 Success measures for predictions by estimated probit model
using y; = 1[F; .5] 50
2.11 Heteroscedastic probit model 56
2.12 Cluster corrected covariance matrix (7,293 groups) 59
2.13 Fixed effects probit model 61
2.14 Estimated fixed effects logit models 65
2.15 Estimated random effects probit models 70
2.16a Semiparametric random effects probit model 70
2.16b Estimated parameters for four class latent class model 70
2.17 Random effects model with Mundlak correction 74
2.18 Estimated random parameter models 78
2.19 Estimated partial effects 78
2.20 Cross-tabulation of healthy and working 86
2.21 Estimated bivariate probit model 87
2.22 Estimated sample selection model 93
2.23 Estimated panel probit model 95
4.1 McCullagh application of an ordered outcome model 130
5.1 Estimated ordered choice models: probit and logit 139
5.2 Estimated partial effects for ordered choice models 144
5.3 Estimated expanded ordered probit model 147
5.4 Transformed latent regression coefficients 150
List of tables
5.5 Estimated partial effects with asymptotic standard errors 158
5.6 Mean predicted probabilities by kids 159
5.7a Predicted vs. actual outcomes for ordered probit model 165
5.7b Predicted probabilities vs. actual outcomes for ordered
probit model 165
5.8 Predicted vs. actual outcomes for automobile data 166
5.9 Grouped data for ordered choice modeling response
frequency in a taste-testing experiment 168
5.10 Estimated ordered choice model based on grouped data 169
5.11 Statu and NLOGIT estimates of an ordered probit model 172
5.12 Software used for ordered choice modeling 179
6.1 Brant test for parameter homogeneity 186
6.2 Estimated ordered logit and generalized ordered logit (1) 191
6.3 Boes and Winkelmann estimated partial effects 194
7.1 Estimated generalized ordered probit models from
Terza(1985) 211
7.2 Estimated hierarchical ordered probit models 217
7.3 Estimated partial effects for ordered probit models 218
7.4 Predicted outcomes from ordered probit models 219
7.5 Estimated heteroscedastic ordered probit model 235
7.6 Partial effects in heteroscedastic ordered probit model 236
8.1 Estimated random parameters ordered probit model 242
8.2 Implied estimates of parameter matrices 243
8.3 Estimated partial effects from random-parameters model 244
8.4 Estimated two-class latent class ordered probit models 256
8.5 Estimated partial effects from latent class models 257
8.6 Estimated generalized random-thresholds ordered logit model 266
9.1 Monte Carlo analysis of the bias of the MLE in fixed-effects
discrete choice models (Means of empirical sampling
distributions, n = 1,000 individuals, R = 200 replications) 270
9.2 Fixed-effects ordered choice models 279
9.3 Random effects ordered logit models - quadrature and
simulation 280
9.4 Random effects ordered probit model with Mundlak correction 281
9.5 Random parameters ordered logit model 283
9.6 Latent-class ordered logit models 284
10.1 Applications of bivariate ordered probit since 2000 296
11.1 Estimated ordered probit sample selection model 310
12.1 Grouping of strike durations 334
12.2 Estimated logistic duration models for strike duration 334
Figures
1.1 IMDb.com ratings (www.imdb.com/title/tt0465234/ratings) page 3
2.1 Random utility basis for a binary outcome 11
2.2 Probability model for binary choice 14
2.3 Probit model for binary choice 15
2.4 Partial effects in a binary choice model 31
2.5 Fitted probabilities for a probit model 39
2.6 Prediction success for different prediction rules 51
2.7 ROC curve for estimated probit model 54
2.8 Distribution of conditional means of income parameter 80
3.1 Underlying probabilities for an ordered choice model 108
4.1 Insecticide experiment 112
4.2 Table of probits for values of p,- 114
4.3 Percentage errors in Pearson table of probability integrals 114
4.4 Implied spline regression in Bliss s probit model 115
5.1 Self-reported health satisfaction 137
5.2 Health satisfaction with combined categories 137
5.3 Estimated ordered probit model 140
5.4a Sample proportions 141
5.4b Implied partitioning of latent normal distribution 141
5.5 Partial effect in ordered probit model 145
5.6 Predicted probabilities for different ages 160
6.1 Estimated partial effects in Boes and Winkelmann (2006b)
models 196
6.2 Estimated partial effects for linear and nonlinear
index functions 196
7.1 Differential item functioning in ordered choices 220
7.2 KMST comparison of political efficacy 229
7.3 KMST estimated vignette model 230
8.1 Kernel density for estimate of the distribution of means ot
income coefficient 247
xS List of figures
12.1 Table 1 from Stewart (2005) 325
12.2 Job satisfaction application, extended 326
12.3 Strike duration data 333
12.4 Estimated nonparametric hazard functions 335
12.5 Estimated hazard function from log-logistic
parametric model 335
|
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| discipline | Politologie Soziologie Psychologie Wirtschaftswissenschaften |
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| spelling | Greene, William 1951- Verfasser (DE-588)124700551 aut Modeling ordered choices a primer William H. Greene and David A. Hensher 1. publ. Cambridge [u.a.] Cambridge Univ. Press 2010 XV, 365 S. graph. Darst. 26 cm txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. [337] - 360 ; Index Choice (Psychology) / Mathematical models Mathematisches Modell Choice (Psychology) Mathematical models Wahl (DE-588)4064286-0 gnd rswk-swf Statistische Entscheidungstheorie (DE-588)4077850-2 gnd rswk-swf Mathematisches Modell (DE-588)4114528-8 gnd rswk-swf Wahl (DE-588)4064286-0 s Mathematisches Modell (DE-588)4114528-8 s DE-604 Statistische Entscheidungstheorie (DE-588)4077850-2 s Hensher, David A. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=020299727&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Greene, William 1951- Hensher, David A. Modeling ordered choices a primer Choice (Psychology) / Mathematical models Mathematisches Modell Choice (Psychology) Mathematical models Wahl (DE-588)4064286-0 gnd Statistische Entscheidungstheorie (DE-588)4077850-2 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| subject_GND | (DE-588)4064286-0 (DE-588)4077850-2 (DE-588)4114528-8 |
| title | Modeling ordered choices a primer |
| title_auth | Modeling ordered choices a primer |
| title_exact_search | Modeling ordered choices a primer |
| title_full | Modeling ordered choices a primer William H. Greene and David A. Hensher |
| title_fullStr | Modeling ordered choices a primer William H. Greene and David A. Hensher |
| title_full_unstemmed | Modeling ordered choices a primer William H. Greene and David A. Hensher |
| title_short | Modeling ordered choices |
| title_sort | modeling ordered choices a primer |
| title_sub | a primer |
| topic | Choice (Psychology) / Mathematical models Mathematisches Modell Choice (Psychology) Mathematical models Wahl (DE-588)4064286-0 gnd Statistische Entscheidungstheorie (DE-588)4077850-2 gnd Mathematisches Modell (DE-588)4114528-8 gnd |
| topic_facet | Choice (Psychology) / Mathematical models Mathematisches Modell Choice (Psychology) Mathematical models Wahl Statistische Entscheidungstheorie |
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