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Re: st: comparing regression coefficients across two models with the same dependent variables


From   Nick Cox <njcoxstata@gmail.com>
To   "statalist@hsphsun2.harvard.edu" <statalist@hsphsun2.harvard.edu>
Subject   Re: st: comparing regression coefficients across two models with the same dependent variables
Date   Fri, 3 May 2013 12:04:56 +0100

Try -glm, link(log) f(poisson)- instead.

Many earlier comments (e.g. from David Hoaglin) still apply, it seems to me.

Nick
njcoxstata@gmail.com

On 3 May 2013 11:18, James Bernard <jamesstatalist@gmail.com> wrote:

> Thanks for your helpful suggestion. It seems -suest- is the
> solution.... but I am getting errors:
>
> But before I elaborate: I got some info form this link and tried the
> steps:     http://www.ats.ucla.edu/stat/stata/code/suest.htm
>
> I am using -suest- on a panel Poisson model
>
> I run model 1 and store its estimates using est store [name 1]
>
> then model 2 and store its estimates as [name2]
>
> and then using -suest  [name1] [name2]
>
> this is the error I get:
>
> unable to generate scores for model low
> suest requires that predict allow the score option
> r(322);
>
> Please note that low is the name of the stored estimates

 On Thu, May 2, 2013 at 10:34 PM, John Antonakis <John.Antonakis@unil.ch> wrote:

>>
>> This is easily and correctly done with Stata's -suest- command. You can test
>> for differences in coefficients across equations as well as sets of
>> coefficients, including intercepts.

On 02.05.2013 16:12, James Bernard wrote:

>>> This may seem simple, but the seems to be no consensus on it. I have
>>> two regressions with the same dependent variables
>>>
>>> I want to compare the same independent variable across these two models:
>>>
>>> Y=a+b0X+b1Z
>>> Y=a+b0X+b1W
>>>
>>> Now, I want to compare b0 across these two models. Any idea if this
>>> can be done in Stata?
>>>
>>> Comparing two coefficients in the same model is rather discussed and
>>> clear, but how about the case I mentioned? (the reason I am doing this
>>> is that I am splitting my sample instead of using interaction effects)
>>>
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