Saving margin estimates in bootstrap program

[Doug Strane]

Hello,

I am attempting to write a bootstrap program that will save the marginal estimates of a multinomial logisitic regression. I would ultimately like my program to create a dataset that contains just the margin estimates obtained from each iteration of my bootstrap program. My current program will save the coefficient estimates for each iteration, but not the margin estimates. Any suggestions on how to achieve that would be helpful.

I am using a complex survey dataset, the Medical Expenditure Panel Survey, in stata 13.1. My outcome is a four-level categorical variable and my exposure variables are year and income level. The (simplified) code I have written is below:

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
program savemargins, rclass
svyset varpsu [pweight=perwt], strata(varstr) psu(varpsu)
svy, subpop(subpop): mlogit outcome i.income##c.year
margins, at [specified levels] predict(outcome(1))
matrix list r(b)
end

bootstrap, saving(margins) reps(200): savemargins
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Thank you for your help.

Best,
Doug

[Clyde Schechter]

It doesn't save the margin estimates because you never tell it to.

program savemargins, rclass 
svyset varpsu [pweight=perwt], strata(varstr) psu(varpsu)
svy, subpop(subpop): mlogit outcome i.income##c.year
margins, at [specified levels] predict(outcome(1))
matrix list r(b)
tempname M
matrix `M' = r(b)
local M_cols = colsof(`M')
forvalues j = 1/`M_cols' {
    return scalar margin_`j' = `M'[1, `j']
}
end

bootstrap m1 = r(margin_1) m2 = r(margin_2) m3 = r(margin_3) /* etc.--you need to enumerate them one by one */,
     saving(margins) reps(200): savemargins

Untested, but I think it will work as is. If not, it should give you a start.

[Dimitriy V. Masterov]

I think you need to post the results of the margins command:

Here's an example, possibly non-sensical:

set more off
use http://www.stata-press.com/data/r14/nhanes2f, clear
svyset psuid [pweight=finalwgt], strata(stratid) psu(psuid)

capture program drop savemargins
program savemargins, rclass
svy, subpop(race): mlogit health i.agegrp##c.zinc
margins, at(zinc=(0 10)) predict(outcome(1)) post
end

bootstrap _b, saving(margins, replace) reps(200): savemargins

[Clyde Schechter]

Dimitry's solution is simpler than mine, but it has one drawback that may preclude using it. After the -margins, ...post- command, the original coefficients from the -mlogit- command are no longer available. If in the real problem -margins- comes at the end of the program, that is not a problem. But if the real program needs to do other things after -margins-, and if those things require the -mlogit- coefficients, then this approach won't work as written.

[Doug Strane]

Thanks so much for your help, Clyde and Dimitriy. I ended up using Clyde's code and it worked just as I hoped it would. Thanks!

[daniel klein]

After the -margins, ...post- command, the original coefficients from the -mlogit- command are no longer available.

I have not much experience with bootstrap, but If this is a problem, it can probably be solved. Try

...
tempname myb
_estimates hold `myb' , copy
margins ... , post
...
_estimates unhold `myb'
...

For more, see

help _estimates

Best
Daniel

[Nader Mehri]

Hi,
Assuming that the health variable in the below solution has missing values, I was wondering to know how can include mi estimate to the below solution to derive the bootstrap margins from the imputed model.
Thanks,
NM

I think you need to post the results of the margins command:

Here's an example, possibly non-sensical:

set more off
use http://www.stata-press.com/data/r14/nhanes2f, clear
svyset psuid [pweight=finalwgt], strata(stratid) psu(psuid)

capture program drop savemargins
program savemargins, rclass
svy, subpop(race): mlogit health i.agegrp##c.zinc
margins, at(zinc=(0 10)) predict(outcome(1)) post
end

bootstrap _b, saving(margins, replace) reps(200): savemargins

[Sur vey]

Hi everyone,
I have a problem similar to post #1. I was wondering if either Clyde's or Dimitry's solutions are applicable to the panel data (long data format). As described here, the randomly selected observations for the bootstrap cannot be chosen by the individual record but must be chosen by the panel. It looks like that neither solutions consider this. I appreciate any feedback.
Thanks SS

Dimitry's solution is simpler than mine, but it has one drawback that may preclude using it. After the -margins, ...post- command, the original coefficients from the -mlogit- command are no longer available. If in the real problem -margins- comes at the end of the program, that is not a problem. But if the real program needs to do other things after -margins-, and if those things require the -mlogit- coefficients, then this approach won't work as written.

Dimitriy V. Masterov[/B]]I think you need to post the results of the margins command:

Here's an example, possibly non-sensical:

Code:
set more off use http://www.stata-press.com/data/r14/nhanes2f, clear svyset psuid [pweight=finalwgt], strata(stratid) psu(psuid) capture program drop savemargins program savemargins, rclass svy, subpop(race): mlogit health i.agegrp##c.zinc margins, at(zinc=(0 10)) predict(outcome(1)) post end bootstrap _b, saving(margins, replace) reps(200): savemargins

[Clyde Schechter]

The -bootstrap- command has a -cluster()- option that allows you to force resampling of panels rather than individual observations. N.B. when you do this, you must also specify the -idcluster()- option. Do read -help bootstrap-, and then follow the link to the PDF manual entry for more information as well.

Please note that it is the norm in this community to use our real given and surnames as our username, to promote collegiality and professionalism. The Forum editor does not permit you to change your user name, however, you can click on Contact Us in the lower right corner of this webpage and message the system administrator to request the change be made. Thank you.

[Sur vey]

Thanks Clyde very much for your helpful comment. Given that xtmlogit is not supported by svy prefix, I wonder to know how can xtset can be applied to define idcluster?

Sure! I will.
Best,
SS

[Lukas Lang]

Thank you Clyde Schechter, Dimitriy V. Masterov for this helpful discussion.

With complex survey data, shouldn't we use bootstrap weights when performing bootstrap?

That can be done easily by using 'bsweights' by skolenik in combination with 'svy boostrap'.

Easy in principle, however.

Following Dimitry's codes in #3, for simplicity, when I try estimating the following:

set more off
use http://www.stata-press.com/data/r14/nhanes2f, clear
svyset psuid [pweight=finalwgt], strata(stratid) psu(psuid)
bsweights bw, reps(200) n(-1) dots replace seed(0123456789) /*to create bootstrap weights*/
svyset psuid [pweight=finalwgt], strata(stratid) psu(psuid) bsrweight(bw*) /*to include bootstrap weights in svyset*/

capture program drop savemargins
program savemargins, rclass
    svy, subpop(race): mlogit health i.agegrp##c.zinc, baseoutcome(1)
    *mlogit health i.agegrp##c.zinc [pw=finalwgt], baseoutcome(1) /*this produces the same point estimates of the command above*/
    margins, at(zinc=(0 10)) predict(outcome(1)) post
end

svy bootstrap _b: savemargins now

I get the following table including the observed coefficient but not the standard errors:

------------------------------------------------------------------------------
             |   Observed   Bootstrap                         Normal-based
             | coefficient  std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1371592          .        .       .            .           .
          2  |   .1233056          .        .       .            .           .
------------------------------------------------------------------------------

Can anyone figure out why?

[Nader Mehri]

I think you need to specify the number of replications you want with your bootstrap which probably is 200 iterations. Also, I think svy prefix for the bootstrap command is redundant as your code already accounts for complex survey design within the program:

set more off
use http://www.stata-press.com/data/r14/nhanes2f, clear
svyset psuid [pweight=finalwgt], strata(stratid) psu(psuid)
bsweights bw, reps(200) n(-1) dots replace seed(0123456789) /*to create bootstrap weights*/
svyset psuid [pweight=finalwgt], strata(stratid) psu(psuid) bsrweight(bw*) /*to include bootstrap weights in svyset*/

capture program drop savemargins
program savemargins, rclass
    svy, subpop(race): mlogit health i.agegrp##c.zinc, baseoutcome(1)
    *mlogit health i.agegrp##c.zinc [pw=finalwgt], baseoutcome(1) /*this produces the same point estimates of the command above*/
    margins, at(zinc=(0 10)) predict(outcome(1)) post
end

 bootstrap _b, reps(200): savemargins now

Bootstrap replications (200)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5 
..................................................    50
..................................................   100
..................................................   150
..................................................   200

Predictive margins                              Number of obs     =      9,188
                                                Replications      =        200

------------------------------------------------------------------------------
             |   Observed   Bootstrap                         Normal-based
             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
         _at |
          1  |   .1371592   .0446705     3.07   0.002     .0496067    .2247117
          2  |   .1233056   .0362283     3.40   0.001     .0522995    .1943117
------------------------------------------------------------------------------

[Lukas Lang]

Thanks Nader Mehri, I had a go with your suggestion and, to test it, I have simplified the codes as follows:

set more off
use http://www.stata-press.com/data/r14/nhanes2f, clear
set seed 0123456789
svyset psuid [pweight=finalwgt], strata(stratid) psu(psuid)
bsweights bw, reps(200) n(-1) dots replace /*to create bootstrap weights*/
svyset psuid [pweight=finalwgt], strata(stratid) psu(psuid) bsrweight(bw*) /*to include bootstrap weights in svyset*/

capture program drop savemargins
program savemargins, rclass
    qui mlogit health i.agegrp##c.zinc [pw=finalwgt], baseoutcome(1) /*this produces the same point estimates of the command above*/
end

 bootstrap _b, reps(200): savemargins now
 svy bootstrap _b: mlogit health i.agegrp##c.zinc, baseoutcome(1)
 svy bootstrap _b: savemargins now

As you can see below, standard errors from 'bootstrap' and 'svy bootstrap' differ:

bootstrap _b, reps(200): savemargins now
(running savemargins on estimation sample)

Bootstrap replications (200)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................    50
..................................................   100
..................................................   150
..................................................   200

Multinomial logistic regression                        Number of obs =   9,188
                                                       Replications  =     200
                                                       Wald chi2(44) = 1227.31
                                                       Prob > chi2   =  0.0000
Log pseudolikelihood = -1.459e+08                      Pseudo R2     =  0.0443

-------------------------------------------------------------------------------
              |   Observed   Bootstrap                         Normal-based
       health | coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
poor          |  (base outcome)
--------------+----------------------------------------------------------------
fair          |
       agegrp |
    age30-39  |   .6307591   2.372338     0.27   0.790    -4.018937    5.280455
    age40-49  |   .1858713    2.25382     0.08   0.934    -4.231535    4.603277
    age50-59  |  -2.285295   1.890361    -1.21   0.227    -5.990334    1.419743
    age60-69  |  -1.267306   1.790183    -0.71   0.479       -4.776    2.241388
     age 70+  |   .0880936   1.984159     0.04   0.965    -3.800786    3.976973
              |
         zinc |   .0030401   .0196409     0.15   0.877    -.0354553    .0415356
              |
agegrp#c.zinc |
    age30-39  |  -.0111744   .0269093    -0.42   0.678    -.0639157    .0415668
    age40-49  |  -.0114104   .0262707    -0.43   0.664    -.0629001    .0400793
    age50-59  |   .0162348   .0221602     0.73   0.464    -.0271985    .0596681
    age60-69  |     .00372   .0206204     0.18   0.857    -.0366953    .0441353
     age 70+  |  -.0123845   .0233368    -0.53   0.596    -.0581238    .0333548
              |
        _cons |   1.467004   1.699347     0.86   0.388    -1.863655    4.797664
--------------+----------------------------------------------------------------
average       |
       agegrp |
    age30-39  |  -1.013213   2.274207    -0.45   0.656    -5.470577    3.444151
    age40-49  |  -1.474417   2.135002    -0.69   0.490    -5.658944     2.71011
    age50-59  |  -4.576451   1.882386    -2.43   0.015    -8.265859   -.8870427
    age60-69  |  -2.953667    1.77805    -1.66   0.097    -6.438581    .5312479
     age 70+  |  -2.122265   1.996448    -1.06   0.288    -6.035231    1.790702
              |
         zinc |  -.0021463   .0196212    -0.11   0.913    -.0406033    .0363106
              |
agegrp#c.zinc |
    age30-39  |   .0070231   .0260274     0.27   0.787    -.0439896    .0580359
    age40-49  |   .0014442   .0247989     0.06   0.954    -.0471607    .0500491
    age50-59  |   .0351228   .0222357     1.58   0.114    -.0084584    .0787039
    age60-69  |   .0103388    .020513     0.50   0.614     -.029866    .0505436
     age 70+  |  -.0032432   .0236537    -0.14   0.891    -.0496036    .0431173
              |
        _cons |   3.336288   1.688949     1.98   0.048     .0260092    6.646566
--------------+----------------------------------------------------------------
good          |
       agegrp |
    age30-39  |  -.4318196   2.222318    -0.19   0.846    -4.787484    3.923844
    age40-49  |  -2.015888   2.220332    -0.91   0.364    -6.367658    2.335883
    age50-59  |  -5.141381   1.852362    -2.78   0.006    -8.771944   -1.510817
    age60-69  |  -3.859092   1.746961    -2.21   0.027    -7.283072   -.4351122
     age 70+  |  -2.179386   1.888796    -1.15   0.249    -5.881358    1.522586
              |
         zinc |   .0069424   .0196188     0.35   0.723    -.0315097    .0453945
              |
agegrp#c.zinc |
    age30-39  |  -.0020289    .025517    -0.08   0.937    -.0520412    .0479834
    age40-49  |   .0013358   .0256616     0.05   0.958    -.0489601    .0516317
    age50-59  |   .0319356   .0217965     1.47   0.143    -.0107848     .074656
    age60-69  |   .0116608   .0201097     0.58   0.562    -.0277536    .0510751
     age 70+  |    -.01292   .0224938    -0.57   0.566     -.057007     .031167
              |
        _cons |   2.965805   1.691762     1.75   0.080    -.3499865    6.281597
--------------+----------------------------------------------------------------
excellent     |
       agegrp |
    age30-39  |  -.4955834   2.281333    -0.22   0.828    -4.966914    3.975747
    age40-49  |  -1.965187   2.196011    -0.89   0.371     -6.26929    2.338916
    age50-59  |  -4.839694   1.972694    -2.45   0.014    -8.706103   -.9732851
    age60-69  |  -3.345652   1.767189    -1.89   0.058    -6.809279    .1179746
     age 70+  |  -3.280243   2.097342    -1.56   0.118    -7.390959    .8304716
              |
         zinc |   .0106708   .0198553     0.54   0.591    -.0282448    .0495864
              |
agegrp#c.zinc |
    age30-39  |  -.0013921   .0260711    -0.05   0.957    -.0524906    .0497064
    age40-49  |   .0018209   .0255722     0.07   0.943    -.0482997    .0519415
    age50-59  |   .0257879   .0232652     1.11   0.268     -.019811    .0713867
    age60-69  |  -.0013309   .0204226    -0.07   0.948    -.0413584    .0386966
     age 70+  |  -.0020064   .0247615    -0.08   0.935    -.0505382    .0465253
              |
        _cons |   2.694864    1.70975     1.58   0.115    -.6561842    6.045912
-------------------------------------------------------------------------------

 
svy bootstrap _b: mlogit health i.agegrp##c.zinc, baseoutcome(1)
(running mlogit on estimation sample)

Bootstrap replications (200)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................    50
..................................................   100
..................................................   150
..................................................   200

Survey: Multinomial logistic regression          Number of obs   =       9,188
                                                 Population size = 104,162,204
                                                 Replications    =         200
                                                 Wald chi2(44)   =    24481.19
                                                 Prob > chi2     =      0.0000

-------------------------------------------------------------------------------
              |   Observed   Bootstrap                         Normal-based
       health | coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
poor          |  (base outcome)
--------------+----------------------------------------------------------------
fair          |
       agegrp |
    age30-39  |   .6307591   2.784013     0.23   0.821    -4.825807    6.087325
    age40-49  |   .1858713    2.58411     0.07   0.943    -4.878892    5.250634
    age50-59  |  -2.285295   2.543675    -0.90   0.369    -7.270807    2.700217
    age60-69  |  -1.267306   2.125213    -0.60   0.551    -5.432647    2.898035
     age 70+  |   .0880936   2.255595     0.04   0.969    -4.332792    4.508979
              |
         zinc |   .0030401   .0241923     0.13   0.900    -.0443758    .0504561
              |
agegrp#c.zinc |
    age30-39  |  -.0111744    .030755    -0.36   0.716    -.0714531    .0491042
    age40-49  |  -.0114104   .0284998    -0.40   0.689    -.0672689    .0444481
    age50-59  |   .0162348   .0291624     0.56   0.578    -.0409224    .0733919
    age60-69  |     .00372   .0236476     0.16   0.875    -.0426284    .0500684
     age 70+  |  -.0123845    .025922    -0.48   0.633    -.0631907    .0384218
              |
        _cons |   1.467004   2.154797     0.68   0.496    -2.756321    5.690329
--------------+----------------------------------------------------------------
average       |
       agegrp |
    age30-39  |  -1.013213   3.008141    -0.34   0.736    -6.909061    4.882635
    age40-49  |  -1.474417   2.662485    -0.55   0.580    -6.692792    3.743957
    age50-59  |  -4.576451   2.427487    -1.89   0.059    -9.334237    .1813353
    age60-69  |  -2.953667   2.391359    -1.24   0.217    -7.640645    1.733312
     age 70+  |  -2.122265   2.516095    -0.84   0.399    -7.053721    2.809192
              |
         zinc |  -.0021463   .0259741    -0.08   0.934    -.0530547     .048762
              |
agegrp#c.zinc |
    age30-39  |   .0070231   .0336488     0.21   0.835    -.0589273    .0729736
    age40-49  |   .0014442   .0297591     0.05   0.961    -.0568826    .0597711
    age50-59  |   .0351228    .027244     1.29   0.197    -.0182744      .08852
    age60-69  |   .0103388   .0270509     0.38   0.702    -.0426799    .0633575
     age 70+  |  -.0032432   .0291694    -0.11   0.911    -.0604142    .0539279
              |
        _cons |   3.336288   2.289978     1.46   0.145    -1.151986    7.824562
--------------+----------------------------------------------------------------
good          |
       agegrp |
    age30-39  |  -.4318196   2.692593    -0.16   0.873    -5.709205    4.845565
    age40-49  |  -2.015888   2.599128    -0.78   0.438    -7.110086     3.07831
    age50-59  |  -5.141381   2.537319    -2.03   0.043    -10.11443   -.1683265
    age60-69  |  -3.859092   2.168088    -1.78   0.075    -8.108467    .3902834
     age 70+  |  -2.179386   2.548777    -0.86   0.393    -7.174897    2.816125
              |
         zinc |   .0069424   .0248134     0.28   0.780    -.0416909    .0555757
              |
agegrp#c.zinc |
    age30-39  |  -.0020289   .0305298    -0.07   0.947    -.0618662    .0578084
    age40-49  |   .0013358    .028949     0.05   0.963    -.0554031    .0580747
    age50-59  |   .0319356   .0288228     1.11   0.268    -.0245561    .0884273
    age60-69  |   .0116608   .0246166     0.47   0.636    -.0365869    .0599084
     age 70+  |    -.01292   .0291148    -0.44   0.657     -.069984     .044144
              |
        _cons |   2.965805   2.177808     1.36   0.173    -1.302619     7.23423
--------------+----------------------------------------------------------------
excellent     |
       agegrp |
    age30-39  |  -.4955834   2.828905    -0.18   0.861    -6.040135    5.048968
    age40-49  |  -1.965187   2.680753    -0.73   0.464    -7.219366    3.288992
    age50-59  |  -4.839694   2.432614    -1.99   0.047    -9.607529    -.071859
    age60-69  |  -3.345652    2.29151    -1.46   0.144     -7.83693    1.145625
     age 70+  |  -3.280243   2.783724    -1.18   0.239    -8.736242    2.175755
              |
         zinc |   .0106708   .0250371     0.43   0.670     -.038401    .0597426
              |
agegrp#c.zinc |
    age30-39  |  -.0013921   .0317513    -0.04   0.965    -.0636235    .0608394
    age40-49  |   .0018209   .0296879     0.06   0.951    -.0563663    .0600081
    age50-59  |   .0257879    .027434     0.94   0.347    -.0279818    .0795575
    age60-69  |  -.0013309   .0255839    -0.05   0.959    -.0514744    .0488125
     age 70+  |  -.0020064   .0320969    -0.06   0.950    -.0649152    .0609024
              |
        _cons |   2.694864   2.207606     1.22   0.222    -1.631964    7.021692
-------------------------------------------------------------------------------

svy bootstrap _b: savemargins now
(running savemargins on estimation sample)

Bootstrap replications (200)
----+--- 1 ---+--- 2 ---+--- 3 ---+--- 4 ---+--- 5
..................................................    50
..................................................   100
..................................................   150
..................................................   200

Multinomial logistic regression                  Number of obs   =      10,337
                                                 Population size = 117,023,659
                                                 Replications    =         200
                                                 Wald chi2(0)    =           .
                                                 Prob > chi2     =           .

-------------------------------------------------------------------------------
              |   Observed   Bootstrap                         Normal-based
       health | coefficient  std. err.      z    P>|z|     [95% conf. interval]
--------------+----------------------------------------------------------------
poor          |  (base outcome)
--------------+----------------------------------------------------------------
fair          |
       agegrp |
    age30-39  |   .6307591          .        .       .            .           .
    age40-49  |   .1858713          .        .       .            .           .
    age50-59  |  -2.285295          .        .       .            .           .
    age60-69  |  -1.267306          .        .       .            .           .
     age 70+  |   .0880936          .        .       .            .           .
              |
         zinc |   .0030401          .        .       .            .           .
              |
agegrp#c.zinc |
    age30-39  |  -.0111744          .        .       .            .           .
    age40-49  |  -.0114104          .        .       .            .           .
    age50-59  |   .0162348          .        .       .            .           .
    age60-69  |     .00372          .        .       .            .           .
     age 70+  |  -.0123845          .        .       .            .           .
              |
        _cons |   1.467004          .        .       .            .           .
--------------+----------------------------------------------------------------
average       |
       agegrp |
    age30-39  |  -1.013213          .        .       .            .           .
    age40-49  |  -1.474417          .        .       .            .           .
    age50-59  |  -4.576451          .        .       .            .           .
    age60-69  |  -2.953667          .        .       .            .           .
     age 70+  |  -2.122265          .        .       .            .           .
              |
         zinc |  -.0021463          .        .       .            .           .
              |
agegrp#c.zinc |
    age30-39  |   .0070231          .        .       .            .           .
    age40-49  |   .0014442          .        .       .            .           .
    age50-59  |   .0351228          .        .       .            .           .
    age60-69  |   .0103388          .        .       .            .           .
     age 70+  |  -.0032432          .        .       .            .           .
              |
        _cons |   3.336288          .        .       .            .           .
--------------+----------------------------------------------------------------
good          |
       agegrp |
    age30-39  |  -.4318196          .        .       .            .           .
    age40-49  |  -2.015888          .        .       .            .           .
    age50-59  |  -5.141381          .        .       .            .           .
    age60-69  |  -3.859092          .        .       .            .           .
     age 70+  |  -2.179386          .        .       .            .           .
              |
         zinc |   .0069424          .        .       .            .           .
              |
agegrp#c.zinc |
    age30-39  |  -.0020289          .        .       .            .           .
    age40-49  |   .0013358          .        .       .            .           .
    age50-59  |   .0319356          .        .       .            .           .
    age60-69  |   .0116608          .        .       .            .           .
     age 70+  |    -.01292          .        .       .            .           .
              |
        _cons |   2.965805          .        .       .            .           .
--------------+----------------------------------------------------------------
excellent     |
       agegrp |
    age30-39  |  -.4955834          .        .       .            .           .
    age40-49  |  -1.965187          .        .       .            .           .
    age50-59  |  -4.839694          .        .       .            .           .
    age60-69  |  -3.345652          .        .       .            .           .
     age 70+  |  -3.280243          .        .       .            .           .
              |
         zinc |   .0106708          .        .       .            .           .
              |
agegrp#c.zinc |
    age30-39  |  -.0013921          .        .       .            .           .
    age40-49  |   .0018209          .        .       .            .           .
    age50-59  |   .0257879          .        .       .            .           .
    age60-69  |  -.0013309          .        .       .            .           .
     age 70+  |  -.0020064          .        .       .            .           .
              |
        _cons |   2.694864          .        .       .            .           .
-------------------------------------------------------------------------------

So I still have some doubts that the 'bootstrap' command takes account of the svyset instructions, at least in the same way 'svy bootstrap' does.

Also, the key issue of no standard errors when running 'svy bootstrap' using a program is still there unfortunately.

Why is that the case?

[Lukas Lang]

Hi I have found a workaround to this issue which you can find here: https://www.statalist.org/forums/for...-wrong-with-it