I want to use the command--sgmediation- to analyze the mediation of panel data.I read that --sgmediation--can not work for panel data.But I think that this command also works for panel data.
Here is my codes.Do you think it is right or wrong?
Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input double(id year y x1 x2 x3)
1 1 3 2 7 3
1 2 2 1 6 4
1 3 3 2 6 5
1 4 4 2 5 6
1 5 5 3 4 7
2 1 6 4 5 8
2 2 5 3 6 8
2 3 5 3 5 7
2 4 4 2 4 6
2 5 3 3 5 5
3 1 4 5 6 4
3 2 5 6 7 3
3 3 6 7 8 6
3 4 5 8 8 7
3 5 4 6 7 8
3 6 3 4 6 9
4 1 6 5 5 8
4 2 5 6 4 7
4 3 6 7 3 6
4 4 7 7 . 5
4 5 8 6 2 4
4 6 9 5 4 3
end
xi:sgmediation y,mv(x2) iv(x1) cv(x3 i.id i.year)
i.id _Iid_1-4 (naturally coded; _Iid_1 omitted)
i.year _Iyear_1-6 (naturally coded; _Iyear_1 omitted)
Model with dv regressed on iv (path c)
Source | SS df MS Number of obs = 21
-------------+---------------------------------- F(10, 10) = 2.63
Model | 41.4558892 10 4.14558892 Prob > F = 0.0718
Residual | 15.7822061 10 1.57822061 R-squared = 0.7243
-------------+---------------------------------- Adj R-squared = 0.4485
Total | 57.2380952 20 2.86190476 Root MSE = 1.2563
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 0.896 0.398 2.25 0.048 0.009 1.782
x3 | -0.170 0.164 -1.04 0.325 -0.534 0.195
_Iid_2 | 0.610 0.922 0.66 0.523 -1.445 2.664
_Iid_3 | -2.615 1.866 -1.40 0.191 -6.773 1.542
_Iid_4 | -0.291 1.857 -0.16 0.879 -4.429 3.846
_Iyear_2 | -0.542 0.889 -0.61 0.556 -2.524 1.439
_Iyear_3 | -0.379 0.937 -0.41 0.694 -2.466 1.707
_Iyear_4 | -0.223 1.005 -0.22 0.829 -2.462 2.016
_Iyear_5 | -0.155 0.910 -0.17 0.868 -2.184 1.873
_Iyear_6 | 1.724 1.254 1.37 0.199 -1.071 4.518
_cons | 2.717 1.254 2.17 0.056 -0.077 5.510
------------------------------------------------------------------------------
Model with mediator regressed on iv (path a)
Source | SS df MS Number of obs = 21
-------------+---------------------------------- F(10, 10) = 3.55
Model | 38.1930886 10 3.81930886 Prob > F = 0.0290
Residual | 10.7592924 10 1.07592924 R-squared = 0.7802
-------------+---------------------------------- Adj R-squared = 0.5604
Total | 48.952381 20 2.44761905 Root MSE = 1.0373
------------------------------------------------------------------------------
x2 | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x1 | 0.307 0.328 0.94 0.372 -0.425 1.039
x3 | 0.014 0.135 0.11 0.918 -0.287 0.315
_Iid_2 | -0.933 0.761 -1.23 0.248 -2.629 0.763
_Iid_3 | 0.122 1.541 0.08 0.938 -3.310 3.555
_Iid_4 | -3.274 1.533 -2.14 0.059 -6.690 0.143
_Iyear_2 | 0.004 0.734 0.00 0.996 -1.632 1.640
_Iyear_3 | -0.484 0.773 -0.63 0.545 -2.207 1.239
_Iyear_4 | -0.843 0.830 -1.02 0.334 -2.691 1.006
_Iyear_5 | -1.407 0.752 -1.87 0.091 -3.082 0.267
_Iyear_6 | -0.353 1.036 -0.34 0.741 -2.660 1.955
_cons | 5.460 1.035 5.27 0.000 3.153 7.767
------------------------------------------------------------------------------
Model with dv regressed on mediator and iv (paths b and c')
Source | SS df MS Number of obs = 21
-------------+---------------------------------- F(11, 9) = 2.24
Model | 41.9107839 11 3.81007126 Prob > F = 0.1184
Residual | 15.3273113 9 1.70303459 R-squared = 0.7322
-------------+---------------------------------- Adj R-squared = 0.4049
Total | 57.2380952 20 2.86190476 Root MSE = 1.305
------------------------------------------------------------------------------
y | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
x2 | -0.206 0.398 -0.52 0.618 -1.106 0.694
x1 | 0.959 0.431 2.23 0.053 -0.016 1.934
x3 | -0.167 0.170 -0.98 0.353 -0.551 0.218
_Iid_2 | 0.418 1.027 0.41 0.694 -1.906 2.741
_Iid_3 | -2.590 1.939 -1.34 0.214 -6.976 1.796
_Iid_4 | -0.964 2.328 -0.41 0.688 -6.229 4.301
_Iyear_2 | -0.542 0.924 -0.59 0.572 -2.631 1.548
_Iyear_3 | -0.479 0.992 -0.48 0.641 -2.722 1.765
_Iyear_4 | -0.397 1.096 -0.36 0.726 -2.877 2.084
_Iyear_5 | -0.445 1.099 -0.40 0.695 -2.931 2.041
_Iyear_6 | 1.651 1.311 1.26 0.239 -1.313 4.616
_cons | 3.839 2.533 1.52 0.164 -1.891 9.569
------------------------------------------------------------------------------
Sobel-Goodman Mediation Tests
Coef Std Err Z P>|Z|
Sobel -.0631524 .13961254 -.4523 .65102373
Goodman-1 (Aroian) -.0631524 .19122088 -.3303 .74120433
Goodman-2 -.0631524 .04917211 -1.284 .19903234
Coef Std Err Z P>|Z|
a coefficient = .307133 .328431 .93515 .349711
b coefficient = -.205619 .39785 -.516825 .605278
Indirect effect = -.063152 .139613 -.45234 .651024
Direct effect = .958806 .430893 2.22516 .02607
Total effect = .895654 .397774 2.25166 .024344
Proportion of total effect that is mediated: -.07050982
Ratio of indirect to direct effect: -.06586565
Ratio of total to direct effect: .93413435
tab id,gen(dum_id)
id | Freq. Percent Cum.
------------+-----------------------------------
1 | 5 22.73 22.73
2 | 5 22.73 45.45
3 | 6 27.27 72.73
4 | 6 27.27 100.00
------------+-----------------------------------
Total | 22 100.00
. tab year,gen(dum_year)
year | Freq. Percent Cum.
------------+-----------------------------------
1 | 4 18.18 18.18
2 | 4 18.18 36.36
3 | 4 18.18 54.55
4 | 4 18.18 72.73
5 | 4 18.18 90.91
6 | 2 9.09 100.00
------------+-----------------------------------
Total | 22 100.00
. bootstrap r(ind_eff) r(dir_eff), reps(500) : sgmediation y, mv(x2) iv(x1) cv(x3 dum_id* dum_year*)
(running sgmediation on estimation sample)
Bootstrap replications (500)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
xxxxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxxxxxxxxxxxx.x.xx 50
xxxxx.xxxxxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxxxxxxxxxx 100
xxxxxxxxx.xxxxxxxxxx.xx.xxxxxxxxx.xxx.xx.xxxxxx.xx 150
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 200
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.xxxxxxxxx. 250
xxxxxx.xxxxxxx.xx..xxxxxxxx.xxxxxxxxxx..xxxxxxxxxx 300
xxxxxxxxxxxxxxxxx.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 350
xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx 400
.xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx.xxxxx 450
xxxxxxxxxx.xxxxxxxxxxxxxxxxxxx.xxxxxxxxxxxx.x.x..x 500
Bootstrap results Number of obs = 21
Replications = 30
command: sgmediation y, mv(x2) iv(x1) cv(x3 dum_id* dum_year*)
_bs_1: r(ind_eff)
_bs_2: r(dir_eff)
------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
_bs_1 | -0.063 0.641 -0.10 0.921 -1.319 1.192
_bs_2 | 0.959 0.911 1.05 0.293 -0.828 2.745
------------------------------------------------------------------------------
Note: One or more parameters could not be estimated in 470 bootstrap
replicates; standard-error estimates include only complete replications.Best
Raymond
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