Limited-Dependent and Qualitative Variables in Econometrics

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Cambridge University Press, Jun 27, 1986 - Business & Economics - 401 pages
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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.
 

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A classic.

Contents

Introduction
1
12 Censored regression models
3
13 Dummy endogenous variables
6
Discrete regression models
13
22 The linear probability model
15
23 The linear discriminant function
16
24 Analogy with multiple regression and the linear probability model
18
25 The probit and logit models
22
69 Truncated regression models
165
610 Endogenous stratification and truncated regression models
170
611 Truncated and censored regression models with stochastic and unobserved thresholds
174
heteroscedasticity
178
613 Problems of aggregation
182
614 Miscellaneous other problems
185
615 A general specification test
192
616 Mixtures of truncated and untruncated distributions
194

26 Comparison of the logit model and normal discriminant analysis
27
27 The twin linear probability model
28
29 Illustrative examples with grouped data
32
unordered variables
34
211 Measures of goodness of fit
37
212 Multinomial logit and McFaddens conditional logit
41
orderedresponse models
46
sequentialresponse models
49
Poisson regression
51
216 Estimation of logit models with randomized data
54
217 Estimation of logit and pro hit models from panel data
56
Probabilisticchoice models
59
32 The Luce model
61
33 The multinomial probit model
62
34 The eliminationbyaspects model
64
35 The hierarchical eliminationbyaspects model
66
36 The nested multinomial logit model
67
37 The generalized extremevalue model
70
38 The relationship between the NMNL model and the GEV model
72
39 Estimation methods
73
310 Goodnessoffit measures
76
311 Some tests for specification error
77
312 Concluding remarks
78
Discriminant analysis
79
43 Prior probabilities and costs of misclassification
80
44 Nonnormal data and logistic discrimination
81
45 The case of several groups
86
46 Bayesian methods
88
47 Separatesample logistic discrimination
90
Multivariate qualitative variables
93
52 Some minimum chisquare methods for grouped data
96
53 Loglinear models
103
54 Conditional logistic models
105
55 Recursive logistic models
108
56 Some comments on LLM CLM RLM the conditional loglinear models and simultaneous equations
113
some consistent and inconsistent models
117
58 Heckmans model with structural shift and dummy endogenous variables
125
59 Unobserved latent variables and dummy indicators
138
510 Summary and conclusions
147
Censored and truncated regression models
149
63 The tobit censored regression model
151
64 A reparametrization of the tobit model
156
65 Twostage estimation of the tobit model
158
66 Prediction in the tobit model
159
67 The twolimit tobit model
160
68 Models of friction
162
Simultaneousequations models with truncated and censored variables
197
72 Simultaneousequations models with truncation and or censoring
199
73 Simultaneousequations models with probit and tobittype selectivity
205
75 The question of logical consistency
214
76 Summary and conclusions
216
Twostage estimation methods
221
83 Twostage methods for switching regression models
223
84 Twostage estimation of censored models
228
85 Twostage estimation of Heckmans model
231
86 Twostage estimation of structural equations
234
87 Probit twostage and tobit twostage methods
240
88 Twostage methods for models with mixed qualitative truncated and continuous variables
242
89 Some alternatives to the twostage methods
247
810 Some final comments
252
Models with selfselectivity
257
92 Selfselection and evaluation of programs
260
93 Selectivity bias with nonnormal distributions
267
94 Some general transformations to normality
272
95 Polychotomouschoice models and selectivity bias
275
96 Multiple criteria for selectivity
278
97 Endogenous switching models and mixturedistribution models
283
98 When can the selection model be used but not the mixture model?
288
99 Summary and conclusions
289
Disequilibrium models
291
102 The Fair and Jaffee model
292
sample separation unknown
296
sample separation known
305
105 Some generalized disequilibrium models
310
106 Price adjustment and disequilibrium
319
107 Models with controlled prices
326
108 Tests for disequilibrium
335
109 Multimarketdisequilibrium models
337
1010 Models for regulated markets and models for centrally planned economies
341
1011 Summary and conclusions
343
Some applications unions and wages
347
112 The AshenfelterJohnson study
348
113 The Schmidt and Strauss study
349
114 Lees binarychoice model
356
115 Alternative specifications of the unionismwages model
359
116 The Abowd and Farber study
362
117 Summary and conclusions
364
Some results on truncated distributions
365
Bibliography
373
Index
397
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About the author (1986)

G. S. Maddala is a University Eminent Professor of Economics at Ohio State University. He attended Andhra University, Bombay University and the University of Chicago. Maddala taught at the University of Florida, the University of Rochester, Stanford University, and Cornell University. He contributed to The Handbook of Econometrics and authored Introduction of Econometrics.