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From |
Nick Cox <[email protected]> |

To |
[email protected] |

Subject |
Re: Re: st: using subgroup regression coefficients in further regressions |

Date |
Mon, 23 Jul 2012 00:48:07 +0100 |

I find it difficult to tell what you want just from your code, and can't follow what you want to do with or without a treatment dummy, but replace yhat = _b[cons] + _b[X1] * X1 + _b[X2] * X2 following a -regress y X1 X2- is unnecessary as that is what predict yhat does. In practice you would need to do something more like this. 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 predict work replace yhat = work if subgroupvar==`i' drop work } On Sun, Jul 22, 2012 at 7:16 PM, Peter Hofmann <[email protected]> wrote: > 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 >>> already exists. >> >>> 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 * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**Re: Re: st: using subgroup regression coefficients in further regressions***From:*Peter Hofmann <[email protected]>

**Re: Re: st: using subgroup regression coefficients in further regressions***From:*Peter Hofmann <[email protected]>

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