December 08, 2005
Logit/Probit
If the dependent variable is binary (two possible values) or categorical, we need a new class of models. The strategy is to find a statistical model that accounts for a discrete dependent variable. Estimating that model then yields parameter estimates and standard errors, which we can analyze using the same techniques we studied for regression models.
The probit model is based on the normal probability density and cdf. It has an elegant mathematical basis, but presented challenges to early computational capabilities.
The logit model has very similar properties, but had the early advantage of being easily differentiable.
There are more versions of these models to explain more complicated choices.
Posted by bparke at December 8, 2005 09:32 PM