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Re: st: Obtaining 95%CI for marginal effect
From
Nur Hafidha Hikmayani <[email protected]>
To
[email protected]
Subject
Re: st: Obtaining 95%CI for marginal effect
Date
Sun, 6 Feb 2011 21:59:14 +0700
Thanks Steve.
I'm afraid however that I'm not clear enough when some independent
variables are categorical. You gave an example in which weight and
turn are numerical variables - in my case, there is only 1 numerical
IV. Suppose foreign is the main IV of interest and other covariates
are mostly categorical, can we use -adjust- too (perhaps with
-svy:prop- beforehand instead)?
hafida-
On Sun, Feb 6, 2011 at 9:12 PM, Steven Samuels <[email protected]> wrote:
> Nur-
>
> I apologize. I checked and discovered -adjust- after -svy: reg- does not
> compute predictions at the weighted means of the covariates, only at the
> unweighted means. As a work-around, you could substitute the weighted means
> by hand.
>
> ****************************
> sysuse auto, clear
> drop if rep78==.
> svyset rep78 [pw=head]
> svy: mean weight turn //get survey weighted means
> xi: svy: reg mpg weight turn i.foreign
> adjust weight= 3138.575 turn=40.33816, by(foreign) ci se
> *****************************
>
> Steve
> [email protected]
>
>
> Nur-
>
> Use -adjust- with the -ci- option. The fitted value of y is not a "marginal
> effect"; for -regress- or (-svy: regress-) the default marginal effects are
> the regression coefficients.
>
> *********************
> sysuse auto, clear
> xi: reg weight price turn i.foreign
> adjust price turn, by(foreign), se ci
> ******************
>
> Steve
> [email protected]
>
> On Feb 6, 2011, at 7:02 AM, Nur Hafidha Hikmayani wrote:
>
> Dear all,
> I've been running some regression models using -svy- and estimating
> its marginal effect using -mfx- (I use Stata 10.1).
> I wonder how can I get the 95% CI for the marginal effects (y)?
>
> The output for regression and its marginal effect are as follows:
>
> . xi: svy: reg GH i.medgrp exgrp chronic nummed gp
> ------------------------------------------------------------------------------
> | Linearized
> GH | Coef. Std. Err. t P>|t| [95% Conf. Interval]
> -------------+----------------------------------------------------------------
> _Imedgrp_1 | 4.429839 3.56263 1.24 0.214 -2.564628 11.42431
> _Imedgrp_2 | 8.333728 3.633545 2.29 0.022 1.200035 15.46742
> _Imedgrp_3 | 10.05818 3.773961 2.67 0.008 2.648813 17.46755
> exgrp | -8.916839 1.89046 -4.72 0.000 -12.62835 -5.205324
> chronic | -10.31767 1.936802 -5.33 0.000 -14.12017 -6.515169
> nummed | -1.063043 .3452676 -3.08 0.002 -1.740902 -.3851831
> gp | -4.347845 1.773649 -2.45 0.014 -7.830027 -.865663
> _cons | 83.19335 3.520136 23.63 0.000 76.28231 90.10438
> ------------------------------------------------------------------------------
>
> . mfx, at(mean _Imedgrp_1=0 _Imedgrp_2=0)
> Marginal effects after svy:regress
> y = Fitted values (predict)
> = 53.085624
> ------------------------------------------------------------------------------
> variable | dy/dx Std. Err. z P>|z| [ 95% C.I. ] X
> ---------+--------------------------------------------------------------------
> _Imedg~1*| 4.429839 3.56263 1.24 0.214 -2.55279 11.4125
> 0
> _Imedg~2*| 8.333728 3.63354 2.29 0.022 1.21211 15.4553
> .379185
> _Imedg~3*| 10.05818 3.77396 2.67 0.008 2.66136 17.455
> 0
> exgrp*| -8.916839 1.89046 -4.72 0.000 -12.6221 -5.21161 .762312
> chronic*| -10.31767 1.9368 -5.33 0.000 -14.1137 -6.52161
> .83752
> nummed | -1.063043 .34527 -3.08 0.002 -1.73975 -.386331 6.69376
> gp*| -4.347845 1.77365 -2.45 0.014 -7.82413 -.871558 .472814
> ------------------------------------------------------------------------------
> (*) dy/dx is for discrete change of dummy variable from 0 to 1
>
>
> Any help is much appreciated,
> Thanks,
> hafida-
>
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