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RE: st: AW: Tobit, negative predictesd values


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: AW: Tobit, negative predictesd values
Date   Tue, 10 Nov 2009 15:44:37 -0000

In a different vein, but I think consistently with Maarten's suggestions: 

1. You are fitting a hyperplane in some space. That could stay entirely above the origin but that's a tall order if many responses are at or very near zero. Perhaps your model could do with some (more?) curvature.... 

2. Otherwise put, if you believe that no predicted responses can plausibly be negative, this functional form won't ensure that. 

3. Plot observed vs predicted and residual vs predicted to get some handles on where your model is misbehaving. 

Nick 
n.j.cox@durham.ac.uk 

Maarten buis

That is perfectly consistent with the logic behind the -tobit-
model: It assumes that there is some latent variable (the ideal
intensity of training), but this can only be realized when this
ideal is larger than some cut-off point, in this case 0. 

The default for the predicted values are these ideal levels of
training (the linear predictor). So, what you found is that 
some employers ideally would want to take training away from 
their employees. If you think that that is not a senisible 
interpretation then the -tobit- may not be the suitable model 
for your situation.

---  Solorzano Mosquera, Jenniffer wrote:

> I estimated a tobit model having intensity labor training
> as dependent variable and a group of firm characteristics
> which are presumpted as determinants of that intensity.
<snip>
> However I've been looking and I found that heavy censoring
> causes these kind of problems on predicted values and even
> worse when high proportion of censored cases is the situation.


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