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


From   Ebru Ozturk <ebru_0512@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Normally distributed error term & testing normality of residuals
Date   Sat, 13 Oct 2012 17:26:20 +0300

Thank you. So, it leaves me with only one choice to follow Cameron and Trivedi. But what would be if unobserved data censored at zero? 

Ebru
 
----------------------------------------
> Date: Sat, 13 Oct 2012 09:42:38 -0400
> Subject: Re: st: Normally distributed error term & testing normality of residuals
> From: jvverkuilen@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> On Sat, Oct 13, 2012 at 9:27 AM, Ebru Ozturk <ebru_0512@hotmail.com> wrote:
> > Thanks. I checked it too. But I just wanted to learn is there a way of testing normality apart from a formal test like histogram, scatter plots?
>
> Ah, yes, I get that point. Well the trick is that the observed data
> are what they are but we don't know the censored data.
>
> I can think of a few "devices" but I'm not sure what good properties
> they would have. Cameron and Trivedi mention (but do not show) the
> notion of comparing the predicted values for non-censored cases to the
> observed values. The distribution won't be normal for them and the
> relationship won't be perfectly linear, but you shouldn't see gross
> differences in a good fitting model.
>
> Example:
>
> sysuse auto
> gen wgt = weight/1000
> tobit mpg wgt, ll(17)
> predict predicted, xb
> predict seforecast, stdf
> gen censored = 0
> replace censored = 1 if mpg <= 17
> summarize mpg predicted if censored == 0, detail
> cor mpg predicted
> qqplot predicted mpg if censored == 0
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