Bookmark and Share

Notice: On March 31, it was announced that Statalist is moving from an email list to a forum. The old list will shut down at the end of May, and its replacement, statalist.org is already up and running.


[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]

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


From   Ebru Ozturk <ebru_0512@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Normally distributed error term & testing normality of
Date   Mon, 15 Oct 2012 10:15:18 +0300

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.
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/faqs/resources/statalist-faq/
> * http://www.ats.ucla.edu/stat/stata/ 		 	   		  
*
*   For searches and help try:
*   http://www.stata.com/help.cgi?search
*   http://www.stata.com/support/faqs/resources/statalist-faq/
*   http://www.ats.ucla.edu/stat/stata/


© Copyright 1996–2014 StataCorp LP   |   Terms of use   |   Privacy   |   Contact us   |   Site index