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Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?


From   "Laura R." <laura.roh@googlemail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: OLS assumptions not met: transformation, gls, or glm as solutions?
Date   Mon, 17 Dec 2012 18:32:33 +0100

Thank you all for your help. I am still a bit confused, because now I
read that also with GLM homoscedasticity and normality of residuals
are assumptions that have to be met. But I will research further on
that type of models in order to find out whether this works better in
my case than OLS.

Laura



2012/12/17 Ryan Kessler <ryan.kessler.stata@gmail.com>:
> The User's Guide is a great place to start. Maarten's point can also
> be illustrated via simulation:
>
> capture program drop ols_sim
> program define ols_sim, rclass
>         version 12
>         syntax [, NONCONstant robust]
>         set obs 300
>         tempvar y x
>         gen `x'=1 in 1/100
>         replace `x'=2 in 101/200
>         replace `x'=3 in 201/300
>
>         if "`nonconstant'"!="" gen `y'=rnormal(`x',`x'^2) in 1/300
>         else gen `y'=rnormal(`x',1) in 1/300
>
>         reg `y' `x', `robust'
>         return scalar beta1=_b[`x']
>         test `x'=1
>         return scalar pv=r(p)
> end
>
> clear
> local reps=1000
> cii `reps' `reps'*0.05
> local v_lb=round(r(lb), 0.001)
> local v_ub=round(r(ub), 0.001)
>
> simulate beta=r(beta1) pv=r(pv), reps(`reps'): ols_sim, nonconstant robust
> qui count if pv <= 0.05
> local rej_rate=`=r(N)'/`reps'
> di "Rejection rate =`rej_rate'     [`v_lb',`v_ub']"
>
> Hope this helps!
>
> Ryan
>
> On Mon, Dec 17, 2012 at 10:27 AM, Maarten Buis <maartenlbuis@gmail.com> wrote:
>> On Mon, Dec 17, 2012 at 4:17 PM, Carlo Lazzaro wrote:
>>> The main meaning of my example is that you cannot be sure, after invoking
>>> -robust-, that heteroskedasticity is automatically  removed. In other words,
>>> homoskedasticity should be checked graphically even after - robust -.
>>
>> Robust standard errors do not change the coefficients, just the
>> standard errors change. So the predicted values and residuals will
>> also remain unchanged after you have specified the -vce(robust)-
>> option. The whole point of robust standard errors is not that it
>> "solves" in some way for heteroskedasticity, it just makes that
>> "assumption" irrelevant. For more, see section 20.20 of the User's
>> Guide.
>>
>> Hope this helps,
>> Maarten
>>
>> ---------------------------------
>> Maarten L. Buis
>> WZB
>> Reichpietschufer 50
>> 10785 Berlin
>> Germany
>>
>> http://www.maartenbuis.nl
>> ---------------------------------
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