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Format: 1 online resource (xi, 401 pages) : , digital, PDF file(s).

ISBN: 1-107-77657-0 , 1-107-77895-6 , 0-511-81017-2

Series Statement: Econometric Society monographs ; 3

Content: This book presents the econometric analysis of single-equation and simultaneous-equation models in which the jointly dependent variables can be continuous, categorical, or truncated. Despite the traditional emphasis on continuous variables in econometrics, many of the economic variables encountered in practice are categorical (those for which a suitable category can be found but where no actual measurement exists) or truncated (those that can be observed only in certain ranges). Such variables are involved, for example, in models of occupational choice, choice of tenure in housing, and choice of type of schooling. Models with regulated prices and rationing, and models for program evaluation, also represent areas of application for the techniques presented by the author.

Note: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , Cover -- Half-title -- Title -- Copyright -- Contents -- Preface -- Chapter 1. Introduction -- 1.1 Truncated regression models -- 1.2 Censored regression models -- 1.3 Dummy endogenous variables -- Chapter 2. Discrete regression models -- 2.1 What are discrete regression models? -- 2.2 The linear probability model -- 2.3 The linear discriminant function -- 2.4 Analogy with multiple regression and the linear probability model -- 2.5 The probit and logit models -- 2.6 Comparison of the logit model and normal discriminant analysis -- 2.7 The twin linear probability model -- 2.8 The case of multiple observations: minimum chi-square methods -- 2.9 Illustrative examples with grouped data -- 2.10 Polychotomous variables: unordered variables -- 2.11 Measures of goodness of fit -- 2.12 Multinomial logit and McFadden's conditional logit -- 2.13 Polychotomous variables: ordered-response models -- 2.14 Polychotomous variables: sequential-response models -- 2.15 Noncategorical variables: Poisson regression -- 2.16 Estimation of logit models with randomized data -- 2.17 Estimation of logit and probit models from panel data -- Chapter 3. Probabilistic-choice models -- 3.1 McFadden's conditional logit model -- 3.2 The Luce model -- 3.3 The multinomial probit model -- 3.4 The elimination-by-aspects model -- 3.5 The hierarchical elimination-by-aspects model -- 3.6 The nested multinomial logit model -- 3.7 The generalized extreme-value model -- 3.8 The relationship between the NMNL model and the GEV model -- 3.9 Estimation methods -- 3.10 Goodness-of-fit measures -- 3.11 Some tests for specification error -- 3.12 Concluding remarks -- Chapter 4. Discriminant analysis -- 4.1 Introduction -- 4.2 The case of two populations -- 4.3 Prior probabilities and costs of misclassification -- 4.4 Nonnormal data and logistic discrimination -- 4.5 The case of several groups. , 4.6 Bayesian methods -- 4.7 Separate-sample logistic discrimination -- Chapter 5. Multivariate qualitative variables -- 5.1 Introduction -- 5.2 Some minimum chi-square methods for grouped data -- 5.3 Log-linear models -- 5.4 Conditional logistic models -- 5.5 Recursive logistic models -- 5.6 Some comments on LLM, CLM, RLM, the conditional log-linear models, and simultaneous equations -- 5.7 Models with mixed structures: some consistent and inconsistent models -- 5.8 Heckman's model with structural shift and dummy endogenous variables -- 5.9 Unobserved latent variables and dummy indicators -- 5.10 Summary and conclusions -- Chapter 6. Censored and truncated regression models -- 6.1 Introduction -- 6.2 Censored and truncated variables -- 6.3 The tobit (censored regression) model -- 6.4 A reparametrization of the tobit model -- 6.5 Two-stage estimation of the tobit model -- 6.6 Prediction in the tobit model -- 6.7 The two-limit tobit model -- 6.8 Models of friction -- 6.9 Truncated regression models -- 6.10 Endogenous stratification and truncated regression models -- 6.11 Truncated and censored regression models with stochastic and unobserved thresholds -- 6.12 Specification errors: heteroscedasticity -- 6.13 Problems of aggregation -- 6.14 Miscellaneous other problems -- 6.15 A general specification test -- 6.16 Mixtures of truncated and untruncated distributions -- Chapter 7. Simultaneous-equations models with truncated and censored variables -- 7.1 Introduction: A general simultaneous-equations model -- 7.2 Simultaneous-equations models with truncation and/or censoring -- 7.3 Simultaneous-equations models with probit- and tobit-type selectivity -- 7.4 Models with mixed latent and observed variables -- 7.5 The question of logical consistency -- 7.6 Summary and conclusions -- Appendix: ML estimation of the supply-and-demand model in section 7.2. , Chapter 8. Two-stage estimation methods -- 8.1 Introduction -- 8.2 Two-stage method for the tobit model -- 8.3 Two-stage methods for switching regression models -- 8.4 Two-stage estimation of censored models -- 8.5 Two-stage estimation of Heckman's model -- 8.6 Two-stage estimation of structural equations -- 8.7 Probit two-stage and tobit two-stage methods -- 8.8 Two-stage methods for models with mixed qualitative, truncated, and continuous variable -- 8.9 Some alternatives to the two-stage methods -- 8.10 Some final comments -- Appendix: Asymptotic covariance matrices for the different two-stage estimators -- Chapter 9. Models with self-selectivity -- 9.1 Introduction -- 9.2 Self-selection and evaluation of programs -- 9.3 Selectivity bias with nonnormal distributions -- 9.4 Some general transformations to normality -- 9.5 Polychotomous-choice models and selectivity bias -- 9.6 Multiple criteria for selectivity -- 9.7 Endogenous switching models and mixture-distribution models -- 9.8 When can the selection model be used, but not the mixture model? -- 9.9 Summary and conclusions -- Chapter 10. Disequilibrium models -- 10.1 Introduction -- 10.2 The Fair and Jaffee model -- 10.3 Maximum-likelihood methods: sample separation unknown -- 10.4 Maximum-likelihood methods: sample separation known -- 10.5 Some generalized disequilibrium models -- 10.6 Price adjustment and disequilibrium -- 10.7 Models with controlled prices -- 10.8 Tests for disequilibrium -- 10.9 Multimarket-disequilibrium models -- 10.10 Models for regulated markets and models for centrally planned economies -- 10.11 Summary and conclusions -- Chapter 11. Some applications: unions and wages -- 11.1 Introduction -- 11.2 The Ashenfelter-Johnson study -- 11.3 The Schmidt and Strauss study -- 11.4 Lee's binary-choice model -- 11.5 Alternative specifications of the unionism-wages model. , 11.6 The Abowd and Farber study -- 11.7 Summary and conclusions -- Appendix: Some results on truncated distributions -- The truncated normal distribution -- Some nonnormal distributions -- Clark's formulas -- A note on computer programs -- Bibliography -- Index. , English

Additional Edition: ISBN 0-521-33825-5

Additional Edition: ISBN 0-521-24143-X

Language: English

URL: Volltext  (lizenzpflichtig)

URL: https://doi.org/10.1017/CBO9780511810176