st: Re: statalist-digest V4 #4057

["Alistair Windsor (U of M)" <alistair.windsor@gmail.com>]

Dear Hafidha,

Perhaps we need to take a step back and ask what Hafidha is looking for. 
As Steve's first post indicates there is no concept of "marginal effect" 
for the dependent variable. The marginal effect is the effect on the 
dependent variable of a change in an independent variable. Ssince this 
is a linear regression this effect is thankfully independent of the 
values of the other variables. Indeed for this linear regression there 
is no need to run mfx since the result is just the same as the 
coefficients and linearized standard errors. the mfxcommand just 
reproduces the results of the regression minus the constant.

To get the standard error of the predicted value at the the means we 
would do the following

mean predicted y = mean y

variance of predicted y at mean x is

x^t sigma^2 (X^t X)^-1 x

where X is the matrix of regressors and x is the vector of means (and a 
1 entry for the constant).

The matrix sigma^2 (X^t X)^-1 is available as e(V) after your reg command.

If you form the appropriate vector x (means) then you should be able to 
get the standard error by doing something like

mat s = x'*e(V)*x
mat lis s

though I am far from an expert in matrix manipulation in Stata.

Hopefully all your categorical variables have been expanded into 0 1 
dummy variables. In this case the means are simply proportions of 1s in 
your data.

Hope that helps,

Alistair



On 2/7/11 1:33 AM, statalist-digest wrote:
> Date: Sun, 6 Feb 2011 19:02:37 +0700
> From: Nur Hafidha Hikmayani<nhhikmayani@gmail.com>
> Subject: st: Obtaining 95%CI for marginal effect
>
> 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-


--
--
Alistair Windsor
380 Dunn Hall           Ph: 901-678-4431
University of Memphis   Fax: 901-678-2480
Memphis, TN 38152-3240
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