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Re: st: Obtaining 95%CI for marginal effect


From   Steven Samuels <sjsamuels@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Obtaining 95%CI for marginal effect
Date   Sun, 6 Feb 2011 09:12:40 -0500

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
sjsamuels@gmail.com


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
sjsamuels@gmail.com

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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