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Re: st: A longitudinal model where the dependent variable is continuous between 0 and 1
From
Nick Cox <[email protected]>
To
"[email protected]" <[email protected]>
Subject
Re: st: A longitudinal model where the dependent variable is continuous between 0 and 1
Date
Mon, 5 Aug 2013 17:15:05 +0100
Sounds good to me. You still need to think very carefully about
standard error assumptions.
Nick
[email protected]
On 5 August 2013 16:57, Najib Mozahem <[email protected]> wrote:
> Hi,
> Thanks for your answer. Since I can use the glm command to model the variable if it takes values from 0 to 1, that should mean that i can use the xtgee command with fam(binomial) and link(logit) to model the same variable if the dataset was longitudinal since generalized estimating equations are a generalization of generalized linear models?
>
> Najib
>
> On Aug 4, 2013, at 6:10 PM, Nick Cox wrote:
>
>> Why tobit? See http://www.stata.com/statalist/archive/2013-08/msg00003.html
>> and particularly the Baum reference there for the main point.
>> Nick
>> [email protected]
>>
>>
>> On 4 August 2013 15:58, Najib Mozahem <[email protected]> wrote:
>>> Hi,
>>> My response variable can take on any value between 0 and 1. The records in my dataset belong to issues from newspapers and each newspaper has several issues, so I would like to take into account the clustering effect. I know about the betafit command with the cluster option, and I also know about the xtmetobit command a set the lower limit to 0 and the upper limit to 1. I would rather use the xtmetobit because I would like to model the random part as well, but I am not sure what to use to model the data.
>>>
>>> Thanks
>>> Najib
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>>
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>
>
> *
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