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# Re: Re: st: using subgroup regression coefficients in further regressions

 From Peter Hofmann <[email protected]> To [email protected] Subject Re: Re: st: using subgroup regression coefficients in further regressions Date Sun, 22 Jul 2012 20:16:13 +0200

```Thanks for the systematic of how to proceed, I think I am close
to the solution by including
- g yhat -    and
- replace yhat... -    and
- treatment_dummy != 1  (no treats included in the reg):

g res = .
g yhat=.
sum subgroupvar, meanonly

qui forvalues i = `r(min)'/`r(max)' {
reg y X1 X2 if subgroupvar==`i' &         treatment_dummy != 1
//reg only control group
predict work, residual
replace res = work if subgroupvar==`i'
drop work
replace yhat = _b[cons] + _b[X1] * X1 + _b[X2] * X2  if subgroupvar==`i'
}

g y = res + yhat   //should give the original dep. var. except for
treatments, right ??

But in my results it seems like the treats are also included in the
reg, so perhaps
& treatment_dummy != 1 did not work....?

The treatments should not be in the same regression as the control group,
since this is quite small and treatments would influence the outcome.
Truly, the constant has to be included.

Thank you
Peter

On Sun, Jul 22, 2012, Nick Cox <[email protected]> wrote:

This looks like a different question to me, but the principles are the same.

0. Initialise a variable to hold results outside the loop

1. After each regression, you use its estimation results. What you
want may be most easily calculated in terms of something like

_b[X1] * X1 + _b[X2] * X2

2. Typically you will -replace- results of the variable created in 0
for some observations only.

However, I don't understand how this differs from a problem best
handled by -predict- or why no constant (intercept) appears in your
expressions.

Nick

On Sun, Jul 22, 2012 at 3:42 PM, Peter Hofmann <[email protected]> wrote:
> Thank you for the fast reply, Nick. Your hint improves my first step, however
> the original question is still unanswered. Obviously I did not pose the
> question clear enough, so I try again:
>
> After the regression I want to use the estimated coefficients (betas) of each
> subgroup (control groups) to calculate the y-hat (=expected dependent
> variable) of the
> treatment observations (each corresponding to its specific subgroup) by:
>
> treatment1:
> yhat_1 = beta-hat1 * X1 + beta-hat2 * X2
>
> treatment2:
> yhat_2 = beta-hat3 * X3 + beta-hat4 * X4
> .....
>
> The calculated y-hats of the treatments can now be compared to the real y's
> of the treatments.
>
> Any help is appreciated!
> Peter

2012/7/22 Peter Hofmann <[email protected]>:
> Thank you for the fast reply, Nick. Your hint improves my first step, however
> the original question is still unanswered. Obviously I did not pose the
> question clear enough, so I try again:
>
> After the regression I want to use the estimated coefficients (betas) of each
> subgroup (control groups) to calculate the y-hat (=expected dependent
> variable) of the
> treatment observations (each corresponding to its specific subgroup) by:
>
> treatment1:
> yhat_1 = beta-hat1 * X1 + beta-hat2 * X2
>
> treatment2:
> yhat_2 = beta-hat3 * X3 + beta-hat4 * X4
> .....
>
> The calculated y-hats of the treatments can now be compared to the real y's
> of the treatments.
>
> Any help is appreciated!
> Peter
>
>
> On Thu, Jul 19, 2012 at 2:00 PM, Nick Cox <[email protected]> wrote:
>
>> That code won't work at all. Apart from some fantasy syntax, the
>> second time around the loop the -generate- would fail as the variable
>
>> But as you want residuals, you can get them directly:
>
>> gen res = .
>> sum subgroupvar, meanonly
>
>> qui forvalues i = `r(min)'/`r(max)' {
>>        reg y x1 x2 if subgroupvar==`i'
>>        predict work, residual
>>         replace res = work if subgroupvar==`i'
>>         drop work
>> }
>
>> Note, if only as a style point, that putting returned results into
>> scalars, and then scalars into locals, is in this case two more steps
>> than needed.
>
>
>
> On Thu, Jul 19, 2012 at 1:12 PM, Peter Hofmann <[email protected]> wrote:
>> Dear all,
>>
>> Currently I use one regression for each subgroup of my control sample
>> and save the subgroup-betas.
>> Now I want to use the respective betas for a regression on the
>> treatment observations that correspond to the respective subgroup (to
>> extract the residuals from these regressions with the treatment
>> values).
>>
>> Currently I use:
>> . sum subgroupvar
>> . scalar min1=r(min)
>> . local j=min1
>> . scalar max1=r(max)
>> . local k=max1
>> . forvalues i=`j'(1)`k' {
>> . reg y x1 x2 if subgroupvar==`i'
>> . mat bhat = e(b)
>> . svmat bhat, names(bhat_`i'_)
>> . }
>>
>> But now I do not know how to proceed:
>> I want to use the respective subgroup betas in a regression on the
>> treatment observations (treatments are indicated by a dummy).
>>
>> I supposed it should look similar to:
>> . forvalues i=`j'(1)`k' {
>> . g yhat = `bhat_*_1' * var1 + `bhat_*_2' * var2    if subgroupvar==`i'
>> . }
>> But that results in:
>> . + invalid name
>> . r(198);
>>
>> I appreciate any help...
>> Peter
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