| 1. | The probit model has been around longer than the logit model.
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| 2. | Ordered logit and ordered probit models are derived under this concept.
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| 3. | The most popular of these is the nested logit model.
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| 4. | The logit model is estimated using the maximum likelihood approach.
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| 5. | Nonlinear models for binary dependent variables include the logit model.
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| 6. | The coefficients obtained from the logit and probit model are fairly close.
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| 7. | In this case, the multinomial probit or multinomial logit technique is used.
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| 8. | For categorical variables with more than two values there is the multinomial logit.
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| 9. | This yields the multinomial logit model ( MNL ).
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| 10. | However, the odds ratio is easier to interpret in the logit model.
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