Re: st: linear probability model (LPM)

[Steven Samuels <ssamuels@albany.edu>]

I should have stated: For proportions in the range .2 to .8 linear,  
logit, and probit models give similar predictions (Cox. Analysis of  
Binary Data, 1970)

On Apr 26, 2007, at 11:52 AM, Steven Samuels wrote:


> A linear probability model is desirable because  effects are risk  
> differences, which are much easier to interpret than odds ratios.  
> It's best  for proportions that are not too close to 0 or 1;  
> otherwise the model may predict probabilities outside those  
> boundaries.  (In this range linear, probit, and logit models give  
> similar predictions-Cox, Analysis of Binary Data, 1972).
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