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st: Obtaining 95%CI for marginal effect
From
Nur Hafidha Hikmayani <[email protected]>
To
[email protected]
Subject
st: Obtaining 95%CI for marginal effect
Date
Sun, 6 Feb 2011 19:02:37 +0700
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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