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Re: st: RE: RE: Residuals in svy:intreg


From   Ángel Rodríguez Laso <angelrlaso@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: RE: Residuals in svy:intreg
Date   Fri, 1 Jun 2012 15:22:30 +0200

Well, at least the analysis of residuals has made me aware that the
outcome needed a transformation, because top censored data were clear
outliers. That has improved the model.

Many thanks again.

Angel Rodriguez-Laso

2012/6/1 Nick Cox <njcoxstata@gmail.com>:
> I can't add much to my previous answer. Clearly you have in mind that
> censoring complicates things. But what does "OK" mean here? That it is
> technically correct? That you cannot be misled? That someone in
> authority might object or assert that you are wrong? I think it
> depends what you are doing, in what circumstances, and who is judging
> on what criteria.
>
> For example, I think it can be helpful to look at residual plots.
> Frequently they don't help, but when they do, it is in an important
> way.
>
> Some economists (in particular) seem to object to anything that is not
> a formal test as arbitrary, subjective and lacking in rigour. So, they
> don't seem to do things like that.
>
> But I can see that any formal procedure that treats residuals from an
> interval regression and ignores their origin is likely to be suspect.
>
> Nick
>
> On Fri, Jun 1, 2012 at 9:43 AM, Ángel Rodríguez Laso
> <angelrlaso@gmail.com> wrote:
>> Then, would it be OK for individuals with censored variables to
>> consider that their observed values are those where censoring took
>> place? Shouldn't there be some 'allowance' for error in the observed
>> values, as I suppose there is when calculating the interval
>> regression?
>>
>> Thank you very much.
>>
>> Angel Rodriguez-Laso
>>
>> 2012/6/1 Nick Cox <njcoxstata@gmail.com>:
>>> I think there are two levels to this.
>>>
>>> For informal (e.g. graphical) analysis, nothing is fundamentally
>>> different, so that absence of pattern is good news and presence of
>>> pattern may make you think about whether the model can be improved.
>>>
>>> For formal analysis, I don't know of any published procedures either,
>>> but if you invented your own, simulation may be the most practical way
>>> to establish their properties.
>>>
>>> On Fri, Jun 1, 2012 at 8:49 AM, Ángel Rodríguez Laso
>>> <angelrlaso@gmail.com> wrote:
>>>> Dear Nick,
>>>>
>>>> Thank you for your answer.
>>>>
>>>> Unfortunately, after intensive search in the web I haven´t been able
>>>> to find any document on the use of residuals in interval regression or
>>>> the checking of assumptions of interval regression in the survey
>>>> setting. Of the two references that Stata manual v11 gives for an
>>>> introduction to interval regression  (Wooldridge, J. M. 2009.
>>>> Introductory Econometrics: A Modern Approach. 4th ed. Cincinnati, OH:
>>>> South-Western. Davidson, R., and J. G. MacKinnon. 2004. Econometric
>>>> Theory and Methods. New York: Oxford University Press.), I've only had
>>>> access to Wooldridge's and it does not say anything on how to use
>>>> residuals in this context.
>>>>
>>>> I suppose the difficult part in calculating (observed-predicted
>>>> values) is assigning values from which censoring takes place as
>>>> observed values for individuals with censored data.
>>>>
>>>> Angel Rodriguez-Laso
>>>>
>>>> 2012/5/31 Nick Cox <n.j.cox@durham.ac.uk>:
>>>>> I doubt that this was the question, but I am assuming here that if you want residuals, then it's just an extra line calculating the residuals as difference between observed and predicted.
>>>>>
>>>>> Nick
>>>>> n.j.cox@durham.ac.uk
>>>>>
>>>>>
>>>>> -----Original Message-----
>>>>> From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Nick Cox
>>>>> Sent: 31 May 2012 11:09
>>>>> To: 'statalist@hsphsun2.harvard.edu'
>>>>> Subject: st: RE: Residuals in svy:intreg
>>>>>
>>>>> Still true in 12.1.
>>>>>
>>>>> I would guess rather that as residuals need some careful interpretation with -intreg-, StataCorp lets users make their own decisions about working with them.
>>>>>
>>>>> If you can refer us to literature defining and using residuals carefully for -intreg-, that would strengthen the case for adding them to official Stata.
>>>>>
>>>>> Nick
>>>>> n.j.cox@durham.ac.uk
>>>>>
>>>>> Ángel Rodríguez Laso
>>>>>
>>>>> I'm working with Stata 9.2 for Windows.
>>>>>
>>>>> I have to carry out an interval regression with survey data, because
>>>>> there are top and bottom censored values. I've noticed Stata version
>>>>> 9.2 does not provide residuals for this model. It calculates predicted
>>>>> values, but if it does not provide (observed-predicted values), there
>>>>> must be a good reason.
>>>>>
>>>>> I understand that, because I'm in a survey environment, I do not have
>>>>> to check for homoskedasticity of residuals and that they are not
>>>>> expected to be independent. But residuals would still be useful to
>>>>> check for model lack of fit (nonlinearity and presence for influential
>>>>> points and outliers). Do you know of any alternatives?
>>>
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