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Re: RE: st: Normally distributed error term & testing normality of


From   "Justina Fischer" <JAVFischer@gmx.de>
To   statalist@hsphsun2.harvard.edu
Subject   Re: RE: st: Normally distributed error term & testing normality of
Date   Mon, 15 Oct 2012 10:11:02 +0200

Hi Ebru,

Jay gave you a very informative and easily to understand answer (IMHO) - if you know what tobit and the alternatives are (econometrically speaking) doing.

I think you have reached a stage where you should take a book of econometrics and try to find out what each 'model' is for and what the underlying assumptions are.

Best

Justina
-------- Original-Nachricht --------
> Datum: Mon, 15 Oct 2012 10:15:18 +0300
> Von: Ebru Ozturk <ebru_0512@hotmail.com>
> An: statalist@hsphsun2.harvard.edu
> Betreff: RE: st: Normally distributed error term & testing normality of

> Do you mean that the model violates the Tobit assumptions in any case and
> I should swithc to a different model?
> 
> Thank you, Ebru
> 
> ----------------------------------------
> > Date: Sun, 14 Oct 2012 21:12:59 -0400
> > Subject: Re: st: Normally distributed error term & testing normality of
> > From: jvverkuilen@gmail.com
> > To: statalist@hsphsun2.harvard.edu
> >
> > On Sun, Oct 14, 2012 at 11:06 AM, Ebru Ozturk <ebru_0512@hotmail.com>
> wrote:
> > >
> > > But, the issue is not for me whether to use -glm- or -tobit-. I want
> to learn how I can test Tobit specifications graphically.
> > >
> > > For instance, in linear regression they generate residuals and check
> it by -qnorm- or -pnorm- (qnorm e). Also, to check heteroscedasticity they
> make a graph of the residuals of the model against the predicted values.
> > >
> > > So, if the residuals will not be normal because of censoring, what
> should I look at? What should I use instead of residuals in Tobit?
> >
> > I think the general problem is that residuals aren't separable from
> > the model, so there is an unavoidable issue of whether to use -glm- or
> > -tobit- at least in any real problem. They are functions of the
> > predictions made and hence conditional on the model. One model may
> > perform much better and thus have better behaved residuals than a
> > different one. Trying to assess this graphically seems like trying to
> > get a family sedan to drive like a sportscar. You can keep working at
> > that problem and maybe get a reasonable approximation to it or you can
> > switch to a model that doesn't make those assumptions.
> >
> > That said, I guess you could beak the problem into two pieces. Piece 1
> > is a probit (or logit) of censored vs. not censored. Assess whether
> > this is fitting reasonably well using the tools for probit models.
> > Piece 2 is conditional on piece 1. The observed data are still not
> > normal, but you might be able to find a reasonable model for them. As
> > I said, I am not confident this would work, but one could try it.
> >
> > One big issue that Tobit model seems to ignore is that the things that
> > predict being censored and the things that predict values for
> > noncensored cases don't have to be the same thing. A model like -zip-
> > (or an adaptation of it) seems like it has some potential in this
> > regard.
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