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RE: st: AW: Tobit, negative predictesd values
Actually, I took as 0's those missing values of the firms whose answer to the question "Did you offered training to your employees this year?" was negative. In fact, when the answer was not they didn't have to answer anything about intensity. In thaht case, intensity only could be larger than 0. But, as Zwick estimated in his paper, he tooks the whole sample, those who answered yes and no in the offering training question. I still don't understand why.
-----Mensaje original-----
De: [email protected] [mailto:[email protected]] En nombre de Austin Nichols
Enviado el: Tuesday, November 10, 2009 11:01 AM
Para: [email protected]
Asunto: Re: st: AW: Tobit, negative predictesd values
If you want predictions in [0,1], a new model is probably in order:
http://www.stata.com/support/faqs/stat/logit.html
On Tue, Nov 10, 2009 at 10:44 AM, Nick Cox <[email protected]> wrote:
> 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
> [email protected]
>
> 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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