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At a Glance
382 Pages
18 x 175 x 247
Paperback
$145.75
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"An outstanding and timely resource on ordered-response choice modeling that takes readers through the history of such models, and develops a clear taxonomy to position the many generalizations and variants of the standard ordered-response model. Bill Greene and David Hensher have done a masterful job in navigating through, and weaving together, the maze of developments and applications in a diversity of fields. The objective perspectives of the literature provide rich guidance for both the researcher as well as the practitioner. This book is literally a one-stop reference resource on ordered-choice modeling that is at once comprehensive, lucid, insightful, and accessible. A `must-have' on the bookshelves of anyone wanting to learn about and apply ordered-choice modeling techniques!" Chandra Bhat, Adan Abou-Ayyash Centennial Professor in Transportation Engineering, University of Texas at Austin
"Theorists and practicing researchers will want to have this text in their working library. It is very well researched, employing examples and interpretation for ordered choice models ranging from the basic to the newest state-of-art methods. I highly recommend this text without reservations." Joseph M. Hilbe, Adjunct Professor of Statistics, Arizona State University
Industry Reviews
'Theorists and practicing researchers will want to have this text in their working library. It is very well researched, employing examples and interpretation for ordered choice models ranging from the basic to the newest state-of-art methods. I highly recommend this text without reservations.' Joseph M. Hilbe, Arizona State University
| List of tables | p. ix |
| List of figures | p. xi |
| Preface | p. xiii |
| Introduction: random utility and ordered choice models | p. 1 |
| Modeling binary choices | p. 9 |
| Random utility formulation of a model for binary choice | p. 10 |
| Probability models for binary choices | p. 11 |
| Estimation and inference | p. 16 |
| Covariance matrix estimation | p. 26 |
| Application of the binary choice model to health satisfaction | p. 28 |
| Partial effects in a binary choice model | p. 30 |
| Hypothesis testing | p. 39 |
| Goodness of fit measures | p. 44 |
| Heteroscedasticity | p. 54 |
| Panel data | p. 57 |
| Parameter heterogeneity | p. 75 |
| Endogeneity of a right-hand-side variable | p. 80 |
| Bivariate binary choice models | p. 83 |
| The multivariate probit and panel probit models | p. 93 |
| Endogenous sampling and case control studies | p. 96 |
| A model for ordered choices | p. 99 |
| A latent regression model for a continuous measure | p. 99 |
| Ordered choice as an outcome of utility maximization | p. 103 |
| An observed discrete outcome | p. 105 |
| Probabilities and the log likelihood | p. 108 |
| Log likelihood function | p. 108 |
| Analysis of data on ordered choices | p. 109 |
| Antecedents and contemporary counterparts | p. 111 |
| The origin of probit analysis: Bliss (1934a), Finney (1947a) | p. 111 |
| Social science data and regression analysis for binary outcomes | p. 116 |
| Analysis of binary choice | p. 117 |
| Ordered outcomes: Aitchison, Silvey (1957), Snell (1964) | p. 117 |
| Minimum chi squared estimation of an ordered response model: Gurland et al. (1960) | p. 123 |
| Individual data and polychotomous outcomes: Walker and Duncan (1967) | p. 125 |
| McKelvey and Zavoina (1975) | p. 127 |
| Cumulative odds model | p. 127 |
| Continuation ratio model | p. 130 |
| The ordered regression model | p. 130 |
| Other related models | p. 132 |
| The latent continuous variable | p. 134 |
| Estimation, inference and analysis using the ordered choice model | p. 136 |
| Application of the ordered choice model to self-assessed health status | p. 136 |
| Distributional assumptions | p. 138 |
| The estimated ordered probit (logit) model | p. 138 |
| The estimated threshold parameters | p. 140 |
| Interpretation of the model - partial effects and scaled coefficients | p. 142 |
| Inference | p. 150 |
| Prediction - computing probabilities | p. 157 |
| Measuring fit | p. 160 |
| Estimation issues | p. 167 |
| Specification issues and generalized models | p. 181 |
| Functional form issues and the generalized ordered choice model (1) | p. 181 |
| Model implications for partial effects | p. 193 |
| Methodological issues | p. 198 |
| Specification tests for ordered choice models | p. 198 |
| Accommodating individual heterogeneity | p. 208 |
| Threshold models - the generalized ordered probit model (2) | p. 209 |
| Nonlinear specifications - a hierarchical ordered probit (HOPIT) model | p. 214 |
| Thresholds and heterogeneity - anchoring vignettes | p. 219 |
| Heterogeneous scaling (heteroscedasticity) of random utility | p. 232 |
| Individually heterogeneous marginal utilities | p. 237 |
| Appendix: Equivalence of the vignette and HOPIT models | p. 237 |
| Parameter variation and a generalized model | p. 239 |
| Random-parameters models | p. 239 |
| Latent class and finite mixture modeling | p. 247 |
| Generalized ordered choice model with random thresholds (3) | p. 262 |
| Ordered choice modeling with panel and time series data | p. 268 |
| Ordered choice models with fixed effects | p. 268 |
| Ordered choice models with random effects | p. 275 |
| Testing for random or fixed effects: a variable addition test | p. 278 |
| Extending parameter heterogeneity models to ordered choices | p. 281 |
| Dynamic models | p. 285 |
| Spatial autocorrelation | p. 289 |
| Bivariate and multivariate ordered choice models | p. 290 |
| Multiple equations | p. 290 |
| Bivariate ordered probit models | p. 291 |
| Polychoric correlation | p. 294 |
| Semi-ordered bivariate probit model | p. 295 |
| Applications of the bivariate ordered probit model | p. 295 |
| A panel data version of the bivariate ordered probit model | p. 297 |
| Trivariate and multivariate ordered probit models | p. 299 |
| Two-part and sample selection models | p. 302 |
| Inflation models | p. 302 |
| Sample selection models | p. 306 |
| An ordered probit model with endogenous treatment effects | p. 319 |
| Semiparametric and nonparametric estimators and analyses | p. 320 |
| Heteroscedasticity | p. 321 |
| A distribution free estimator with unknown heteroscedasticity | p. 323 |
| A semi-nonparametric approach | p. 324 |
| A partially linear model | p. 327 |
| Semiparametric analysis | p. 327 |
| A nonparametric duration model | p. 329 |
| References | p. 337 |
| Index | p. 361 |
| Table of Contents provided by Ingram. All Rights Reserved. |
ISBN: 9780521142373
ISBN-10: 0521142377
Published: 8th April 2010
Format: Paperback
Language: English
Number of Pages: 382
Audience: Professional and Scholarly
Publisher: Cambridge University Press
Country of Publication: GB
Dimensions (cm): 18 x 175 x 247
Weight (kg): 756
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