Logit, Probit and Tobit: Models for Categorical and Limited ...

The Logit and Probit Models
• However However, the parameters of the two models are
scaled differently. The parameter estimates in a
logistic g regression g tend to be 1.6 to 1.8 times higher g
than they are in a corresponding probit model.
• The probit p and logit g models are estimated by y
maximum likelihood (ML), assuming independence
across observations. The ML estimator of β is
consistent i and dasymptotically i ll normally ll distributed. di ib d
However, the estimation rests on the strong
assumption that the latent error term is normally
distributed and homoscedastic. If homoscedasticity is
violated, , no easy ysolution.