Logit Models of Individual Choice
Thierry Magnac
Abstract
Thierry Magnac
Abstract
The logit model was named by Berkson after probit, its close competitor; the two are the most popular econometric methods used in applied work to estimate models for binary variables. It can be easily extended to the treatment of multinomial variables and enjoys specific properties in panel data binary models. Increasingly flexible logit models have also been elaborated for demand analyses. Their development has been stimulated by the increasing availability of databases on individual discrete choices. Because generalized logit models belong to the class of random utility models, their use has promoted sound applied economic research in demand analysis.
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The logit model was named by Berkson after probit, its close competitor; the two are the most popular econometric methods used in applied work to estimate models for binary variables. It can be easily extended to the treatment of multinomial variables and enjoys specific properties in panel data binary models. Increasingly flexible logit models have also been elaborated for demand analyses. Their development has been stimulated by the increasing availability of databases on individual discrete choices. Because generalized logit models belong to the class of random utility models, their use has promoted sound applied economic research in demand analysis.
Key concepts: Multinomial probit, Multinomial logistic regression, Logit, Mixed logit, Probit, Ordered probit, Discrete choice, Econometrics