error when using bsample with weights

[Luis Mijares Castaneda]

I'm reposting this question because I never got any response

Hello, I am trying to manually implement the survey design to compute standard errors using the -arhomme- command since svyset is not compatible with it. I am using bsample with the options strata(raestrat) cluster(raehsamp) weight(new_weight), but when I include the weight() option, I receive the error: "estimates post: matrix has missing values." Any advice on how to properly implement the weights would be greatly appreciated. My code is listed below.

****bootstrap survey design SE*****
*Step 1, save observed coefficients****
preserve
quietly xi: arhomme log_avrg_cost i.inc_d i.endentulism i.race i.age_cat i.male i.education i.veteran i.mothered i.wealth i.smoke_now chronicdisease[pw=new_weight], select(r11dentst = dentalinsurance_w1 endentulism inc_d race age_cat male education veteran mothered wealth smoke_now chronicdisease)  quantiles(0.10, 0.25, 0.50, 0.75, 0.90)  taupoints(29) rhopoints(35)  meshsize(0.5) frank nostderrors  centergrid(-0.20)

matrix beta = e(b)
global N "e(N)'"
global Ns "e(sN)'"
restore

** step 2 generate program for bootstrap****
capture program drop arhomme_bootstrap
program arhomme_bootstrap, eclass
preserve
bsample, strata(raestrat) cluster(raehsamp) weight(new_weight)

quietly xi:  arhomme log_avrg_cost i.inc_d i.endentulism i.race i.age_cat i.male i.education i.veteran i.mothered i.wealth i.smoke_now chronicdisease[pw=new_weight], select(r11dentst = dentalinsurance_w1 endentulism inc_d race age_cat male education veteran mothered wealth smoke_now chronicdisease) quantiles(0.10, 0.25, 0.50, 0.75, 0.90)  taupoints(29) rhopoints(35)  meshsize(0.5) frank nostderrors  centergrid(-0.20)

matrix beta_boot = e(b)

forvalue i = 1/119 {
    
    ereturn scalar beta_boot_i' =beta_boot[1, i']
}

restore
end  

estimates post: matrix has missing values
r(504);

[Felix Bittmann]

Debugging this is difficult as we do not have the data to test your command. But I think there is something wrong how you reference the local. It should be:

ereturn scalar beta_boot_`i' =beta_boot[1, `i']

As you access e(b), you can return the matrix as a whole in eclass, see:

cap program drop booter
program define booter, eclass
reg mpg weight length foreign
matrix a = e(b)
ereturn post a
end



bootstrap, seed(123): booter
estat bootstrap

[Luis Mijares Castaneda]

my apologies, here is the dataex

* Example generated by -dataex-. For more info, type help dataex
clear
input float log_avrg_cost byte r11dentst float(dentalinsurance_w1 endentulism inc_d race age_cat) byte(male education veteran) float mothered byte wealth float(smoke_now chronicdisease new_weight) byte(raestrat raehsamp)
 5.860786 1 0 0 0 1 3 0 5 0 1 4 0 3  4918.557 40 1
        . 1 1 0 0 1 1 0 5 0 1 4 0 3  6623.596  1 2
 6.478509 1 1 0 0 1 2 0 3 0 0 4 0 0  5815.177  1 2
 7.824446 1 0 0 0 1 2 0 4 0 1 4 1 0  5893.801  1 2
 6.398595 1 1 0 0 1 3 1 5 1 1 4 0 2  6132.698  2 2
 8.699764 1 1 0 0 1 2 0 5 0 0 4 0 0   6568.66  2 2
 5.525453 1 1 0 0 4 3 0 5 0 1 4 0 2  6938.598  2 2
        . 0 1 0 0 3 3 0 1 0 . 2 0 2 2601.5085  1 2
 6.685861 1 1 0 0 3 3 0 1 0 0 1 0 2  3354.929  1 2
 6.398595 1 1 0 0 1 2 0 1 0 1 4 0 1   7329.36  1 1
 7.378384 1 0 0 0 1 4 1 5 1 0 4 0 2  6378.651  1 1
        . 1 1 0 0 3 3 0 1 0 0 1 0 2  8065.042  1 2
        . 0 1 1 0 4 3 1 1 0 0 1 0 3  5591.879  1 2
        . 1 1 1 0 2 3 0 3 0 0 1 0 1  7468.239  2 2
        . 0 0 0 0 2 3 1 1 0 . 1 0 0  6987.121  2 2
 7.151485 1 1 0 0 1 3 0 4 0 1 1 0 1  4896.986  1 2
        . 0 1 1 1 2 3 0 1 0 . 1 0 1  3817.199  1 2
 6.216606 1 1 0 0 1 3 0 4 0 1 4 0 0  5999.094  1 1
 5.993961 1 1 0 0 1 3 1 5 1 1 4 0 0  6925.122  1 1
  8.03948 1 0 0 0 1 3 0 1 0 1 4 0 1  5896.825  1 1
        . 0 0 0 0 1 3 0 3 0 1 1 0 2  8667.065  2 1
 5.303305 1 1 1 0 4 3 0 5 0 0 4 0 2  6781.349  2 2
 5.303305 0 1 0 0 4 3 1 5 1 0 4 0 3  6224.931  2 2
 4.620059 1 1 0 0 1 3 0 3 0 0 1 0 0  6747.077  2 2
        . 0 1 1 0 1 3 1 4 1 . 2 0 1  6543.964  1 2
 7.313887 1 1 0 0 1 2 0 5 0 1 2 0 0  6165.962  1 2
 8.881975 1 0 0 0 1 4 0 5 0 1 4 0 2   3695.68  2 1
 5.993961 1 0 0 0 1 3 1 5 1 1 4 0 2  6698.451  2 1
 7.824446 1 0 0 0 1 3 0 3 0 1 4 0 0  6189.557  2 1
 7.523481 1 1 0 0 1 3 1 5 0 0 4 0 3  6313.635  2 1
 6.803505 1 1 0 0 1 3 0 5 0 1 4 0 1  7654.785  2 1
        . 1 1 0 0 2 3 0 1 0 0 3 0 2 2294.2178  2 1
        . 0 0 0 0 2 3 0 5 0 1 1 0 2 2306.8857  2 1
        . 0 0 1 0 2 3 0 1 0 0 4 0 1  2917.955  2 2
        . 0 0 1 0 2 2 0 3 0 0 1 0 0 2076.2468  1 2
 6.398595 1 1 0 0 2 3 1 3 1 1 3 0 3 3628.3115  1 2
        . 1 1 0 0 2 3 0 3 0 1 3 0 0 1960.0643  1 2
 6.908755 0 0 0 0 2 3 1 3 0 0 2 . 2  6003.062  1 2
 5.786897 1 1 0 0 3 1 0 3 0 1 4 0 0  6675.004  4 1
 4.836282 1 1 0 0 4 3 0 1 0 0 4 0 0  5975.286  4 1
 5.303305 1 1 0 0 4 4 1 5 0 0 4 0 0  6322.484  4 1
 6.908755 1 1 0 0 3 3 1 4 1 1 4 0 1 3176.7256  3 2
 3.314186 1 1 0 0 1 2 0 4 0 1 4 0 0  3072.397  3 2
  7.31422 1 0 0 0 1 2 0 4 0 0 4 0 1  6713.827  3 2
 8.987322 1 1 0 0 2 3 0 5 0 0 1 0 4 3192.3496  4 1
        . 0 1 1 0 4 3 0 4 0 0 2 0 0  3568.704  3 1
 4.912655 1 1 0 1 1 3 1 1 0 1 2 0 1  6398.812  4 2
  5.86221 1 1 0 0 1 3 0 5 0 1 2 0 1  6481.468  4 2
 5.239098 1 1 0 0 1 3 1 3 1 0 4 0 2  6808.061  3 2
        . 0 0 0 0 2 3 0 4 0 0 3 0 3 11825.906  3 2
 6.803505 1 1 0 0 1 4 1 5 1 1 4 0 2  7366.496  4 1
 7.438972 1 0 0 0 3 3 0 3 0 0 3 0 1   3320.87  3 2
        . 0 0 1 0 3 3 1 2 1 0 3 0 1  3323.894  3 2
4.6151204 0 0 0 0 4 4 1 5 0 0 2 0 1 1206.5812  4 2
3.9318256 1 0 0 0 4 3 0 5 0 0 2 0 2 1571.9827  4 2
        . 0 0 0 0 2 3 0 1 0 0 1 1 2  5680.608  4 2
 5.351858 1 0 0 0 2 3 0 3 0 0 1 0 1  4061.249  4 2
        . 0 0 0 0 2 3 0 1 0 . 1 0 2   3636.16  4 2
  6.53814 1 0 0 0 1 3 1 4 1 0 1 0 3 3392.4385  4 2
 7.313887 1 1 0 1 4 2 0 1 0 1 2 0 6   3709.96  4 2
 8.101981 1 0 0 0 1 4 1 3 1 1 4 0 4  5359.752  5 2
  6.29803 1 0 0 0 1 3 0 1 0 1 4 0 1  3885.647  5 2
 6.055613 1 1 0 0 1 3 0 5 0 1 3 0 1  4188.258  5 2
        . 0 0 0 0 1 3 0 3 0 1 2 0 2  3567.327  5 2
  7.91972 1 0 0 0 1 4 0 4 0 0 4 0 3  3992.201  6 2
 6.908755 1 1 0 0 1 4 1 5 1 . 4 0 1  3890.897  6 2
 7.003974 1 0 0 0 1 4 1 4 1 1 4 0 1 4450.5884  5 1
 6.763885 1 0 0 0 1 3 0 3 0 0 4 0 4 4880.6187  5 1
        . 1 1 0 0 1 2 0 3 0 1 2 0 1  4865.739  5 1
        . 1 1 0 0 1 3 0 2 0 0 3 0 0   4232.61  6 1
1.7917595 1 0 0 0 3 3 0 3 0 . 1 0 3 1945.3035  5 2
        . 0 0 1 0 2 3 0 4 0 1 2 0 3  2502.309  6 2
        . 0 0 0 0 1 3 0 1 0 0 1 0 2  4858.299  5 1
 6.685861 1 1 0 0 1 3 1 4 1 1 2 0 1 4108.6255  6 1
 5.463832 1 0 0 0 1 3 1 3 0 . 3 0 2  4545.595  6 1
        . 0 0 0 0 1 3 0 3 0 1 4 0 1 4109.1294  6 2
 4.620059 1 1 0 0 1 4 1 1 1 0 3 0 0 4699.8203  5 2
 6.634634 1 0 0 0 1 3 1 5 1 1 4 0 2  2464.117  5 2
 5.525453 0 1 0 0 1 3 1 5 0 0 4 0 2  3485.679  6 1
 5.860786 1 1 0 0 1 1 0 5 0 1 4 0 0  4411.027  6 1
        . 0 0 1 0 1 3 1 2 0 0 3 0 3  4098.926  6 1
 6.246107 1 0 0 0 3 2 0 1 0 0 3 . 2  4772.491  6 1
        . 0 1 0 0 1 4 1 1 0 0 2 0 3 4599.3877  6 2
 5.673323 1 0 0 0 1 4 0 4 0 0 3 0 0  3694.336  6 2
        . 0 0 1 0 2 3 0 3 0 0 1 0 3  2498.341  5 1
        . 0 0 0 0 2 3 1 2 0 1 1 1 0  5680.104  5 2
        . 0 0 1 1 2 3 1 3 0 1 2 1 0  3492.735  6 2
        . 0 0 0 0 2 3 0 4 0 0 2 0 2  2664.004  5 1
 3.433987 1 1 0 0 2 3 1 4 1 0 3 0 2  1660.687  5 2
3.9318256 1 1 0 1 2 1 0 4 0 0 3 1 2 1695.4633  5 2
 5.170484 1 0 0 0 2 3 1 1 0 1 2 0 3  4255.165  7 2
        . 0 0 1 0 1 3 1 1 1 0 3 0 4  5537.976  7 1
 7.601402 1 0 0 0 1 3 0 1 0 0 4 0 0  5666.496  7 1
        . 0 0 1 0 2 3 1 1 1 . 2 0 3   4951.05  7 1
        . 0 0 0 0 2 2 0 3 0 1 3 0 1 1885.2877  7 2
        . 0 0 1 0 1 2 0 3 0 . 2 1 2 16301.464  7 1
        . 0 0 1 1 1 3 1 4 0 1 4 0 4  5449.032  7 1
        . 0 0 0 0 1 2 0 3 0 1 4 0 0  5753.079  7 1
  5.01728 1 1 0 0 1 3 0 4 0 1 1 0 3 4752.1553  7 2
        . 0 0 0 0 1 3 0 3 0 0 3 0 4 4227.8853  7 2
end
label values r11dentst YESNO
label def YESNO 0 "0.no", modify
label def YESNO 1 "1.yes", modify
label values dentalinsurance_w1 dentalinsurance_w1
label def dentalinsurance_w1 0 "No", modify
label def dentalinsurance_w1 1 "Yes", modify
label values endentulism endentulism
label def endentulism 0 "No", modify
label def endentulism 1 "Yes", modify
label values inc_d inc_d
label def inc_d 0 "No", modify
label def inc_d 1 "Yes", modify
label values race race
label def race 1 "White", modify
label def race 2 "Black", modify
label def race 3 "Hispanic", modify
label def race 4 "Other", modify
label values age_cat age_cat
label def age_cat 1 "50-59", modify
label def age_cat 2 "60-69", modify
label def age_cat 3 "70-79", modify
label def age_cat 4 "80+", modify
label values male male
label def male 0 "Female", modify
label def male 1 "Male", modify
label values education EDUC
label def EDUC 1 "1.lt high-school", modify
label def EDUC 2 "2.ged", modify
label def EDUC 3 "3.high-school graduate", modify
label def EDUC 4 "4.some college", modify
label def EDUC 5 "5.college and above", modify
label values veteran veteran
label def veteran 0 "No", modify
label def veteran 1 "Yes", modify
label values mothered mothered
label def mothered 0 "Less than High School", modify
label def mothered 1 "High School or Higher", modify
label values smoke_now smoke_now
label def smoke_now 0 "Non-Smoker", modify
label def smoke_now 1 "Currently Smokes", modify

[Felix Bittmann]

I think one of the main issues here is that the bsample weighs only accept frequency weights and strata in svy and strata in bsample mean something different. I would argue that bsample is not the main issue here. Rather, arhomme needs to work with these weight aspects as an estimation command. Maybe someone else has some deeper insight on this.

[Luis Mijares Castaneda]

I did change from bsample to gsample to incorporate weights, my new code is shown below, I'm just not confident that I'm calculating the SEs properly

 
****bootstrap survey design SE***** 
*Step 1, save observed coefficients**** 
preserve
quietly xi: arhomme log_avrg_cost i.inc_d i.endentulism i.race i.age_cat i.male i.education i.veteran i.mothered i.wealth i.smoke_now chronicdisease[pw=new_weight], select(r11dentst = dentalinsurance_w1 endentulism inc_d race age_cat male education veteran mothered wealth smoke_now chronicdisease) quantiles(0.10, 0.25, 0.50, 0.75, 0.90)  taupoints(29) rhopoints(35)  meshsize(0.5) frank nostderrors  centergrid(-0.20)

matrix beta = e(b) 
global N "`e(N)'"
global Ns "`e(sN)'" 
restore 

** step 2 generate program for bootstrap****
capture program drop arhomme_bootstrap 
program arhomme_bootstrap, eclass
preserve
gsample [w=new_weight], strata(raestrat) cluster(raehsamp)

quietly xi:  arhomme log_avrg_cost i.inc_d i.endentulism i.race i.age_cat i.male i.education i.veteran i.mothered i.wealth i.smoke_now chronicdisease[pw=new_weight], select(r11dentst = dentalinsurance_w1 endentulism inc_d race age_cat male education veteran mothered wealth smoke_now chronicdisease) quantiles(0.10, 0.25, 0.50, 0.75, 0.90)  taupoints(29) rhopoints(35)  meshsize(0.5) frank nostderrors  centergrid(-0.20)

matrix beta_boot = e(b)

forvalue i = 1/119 { 
    
    ereturn scalar beta_boot_`i' =beta_boot[1, `i']
}


restore 
end 

* step 3: run the bootstrap** 


preserve 

simulate b_r11dentst_dental_insurance = beta_boot[1,1] b_r11dentst_endentulism = beta_boot[1,2] b_r11dentst_inc_d = beta_boot[1,3] b_r11dentst_race = beta_boot[1,4] b_r11dentst_age_cat = beta_boot[1,5] ///
b_r11dentst_male = beta_boot[1,6] b_r11dentst_education = beta_boot[1,7] b_r11dentst_veteran = beta_boot[1,8] b_r11dentst_mothered = beta_boot[1,9] b_r11dentst_wealth = beta_boot[1,10] ///
b_r11dentst_smoke_now = beta_boot[1,11] b_r11dentst_chronicdisease = beta_boot[1,12] b_r11dentst_cons = beta_boot[1,13] b_q10_cons = beta_boot[1,14] b_q10_inc_d = beta_boot[1,15] ///
b_q10_endentulism = beta_boot[1,16] b_q10_race_2 = beta_boot[1,17] b_q10_race_3 = beta_boot[1,18] b_q10_race_4 = beta_boot[1,19] b_q10_age_cat_2 = beta_boot[1,20] ///
b_q10_age_cat_3 = beta_boot[1,21] b_q10_age_cat_4 = beta_boot[1,22] b_q10_male = beta_boot[1,23] b_q10_education_2 = beta_boot[1,24] b_q10_education_3 = beta_boot[1,25] ///
b_q10_education_4 = beta_boot[1,26] b_q10_education_5 = beta_boot[1,27] b_q10_veteran = beta_boot[1,28] b_q10_mothered = beta_boot[1,29] b_q10_wealth_2 = beta_boot[1,30] ///
b_q10_wealth_3 = beta_boot[1,31] b_q10_wealth_4 = beta_boot[1,32] b_q10_smoke_now = beta_boot[1,33] b_q10_chronicdisease = beta_boot[1,34] b_q25_cons = beta_boot[1,35] ///
b_q25_inc_d = beta_boot[1,36] b_q25_endentulism = beta_boot[1,37] b_q25_race_2 = beta_boot[1,38] b_q25_race_3 = beta_boot[1,39] b_q25_race_4 = beta_boot[1,40] ///
b_q25_age_cat_2 = beta_boot[1,41] b_q25_age_cat_3 = beta_boot[1,42] b_q25_age_cat_4 = beta_boot[1,43] b_q25_male = beta_boot[1,44] b_q25_education_2 = beta_boot[1,45] ///
b_q25_education_3 = beta_boot[1,46] b_q25_education_4 = beta_boot[1,47] b_q25_education_5 = beta_boot[1,48] b_q25_veteran = beta_boot[1,49] b_q25_mothered = beta_boot[1,50] ///
b_q25_wealth_2 = beta_boot[1,51] b_q25_wealth_3 = beta_boot[1,52] b_q25_wealth_4 = beta_boot[1,53] b_q25_smoke_now = beta_boot[1,54] b_q25_chronicdisease = beta_boot[1,55] ///
b_q50_cons = beta_boot[1,56] b_q50_inc_d = beta_boot[1,57] b_q50_endentulism = beta_boot[1,58] b_q50_race_2 = beta_boot[1,59] b_q50_race_3 = beta_boot[1,60] ///
b_q50_race_4 = beta_boot[1,61] b_q50_age_cat_2 = beta_boot[1,62] b_q50_age_cat_3 = beta_boot[1,63] b_q50_age_cat_4 = beta_boot[1,64] b_q50_male = beta_boot[1,65] ///
b_q50_education_2 = beta_boot[1,66] b_q50_education_3 = beta_boot[1,67] b_q50_education_4 = beta_boot[1,68] b_q50_education_5 = beta_boot[1,69] b_q50_veteran = beta_boot[1,70] ///
b_q50_mothered = beta_boot[1,71] b_q50_wealth_2 = beta_boot[1,72] b_q50_wealth_3 = beta_boot[1,73] b_q50_wealth_4 = beta_boot[1,74] b_q50_smoke_now = beta_boot[1,75] ///
b_q50_chronicdisease = beta_boot[1,76] b_q75_cons = beta_boot[1,77] b_q75_inc_d = beta_boot[1,78] b_q75_endentulism = beta_boot[1,79] b_q75_race_2 = beta_boot[1,80] ///
b_q75_race_3 = beta_boot[1,81] b_q75_race_4 = beta_boot[1,82] b_q75_age_cat_2 = beta_boot[1,83] b_q75_age_cat_3 = beta_boot[1,84] b_q75_age_cat_4 = beta_boot[1,85] ///
b_q75_male = beta_boot[1,86] b_q75_education_2 = beta_boot[1,87] b_q75_education_3 = beta_boot[1,88] b_q75_education_4 = beta_boot[1,89] b_q75_education_5 = beta_boot[1,90] ///
b_q75_veteran = beta_boot[1,91] b_q75_mothered = beta_boot[1,92] b_q75_wealth_2 = beta_boot[1,93] b_q75_wealth_3 = beta_boot[1,94] b_q75_wealth_4 = beta_boot[1,95] ///
b_q75_smoke_now = beta_boot[1,96] b_q75_chronicdisease = beta_boot[1,97] b_q90_cons = beta_boot[1,98] b_q90_inc_d = beta_boot[1,99] b_q90_endentulism = beta_boot[1,100] ///
b_q90_race_2 = beta_boot[1,101] b_q90_race_3 = beta_boot[1,102] b_q90_race_4 = beta_boot[1,103] b_q90_age_cat_2 = beta_boot[1,104] b_q90_age_cat_3 = beta_boot[1,105] ///
b_q90_age_cat_4 = beta_boot[1,106] b_q90_male = beta_boot[1,107] b_q90_education_2 = beta_boot[1,108] b_q90_education_3 = beta_boot[1,109] b_q90_education_4 = beta_boot[1,110] ///
b_q90_education_5 = beta_boot[1,111] b_q90_veteran = beta_boot[1,112] b_q90_mothered = beta_boot[1,113] b_q90_wealth_2 = beta_boot[1,114] b_q90_wealth_3 = beta_boot[1,115] ///
b_q90_wealth_4 = beta_boot[1,116] b_q90_smoke_now = beta_boot[1,117] b_q90_chronicdisease = beta_boot[1,118] b_anc_rho = beta_boot[1,119], /// 
reps(50) seed(123456): arhomme_bootstrap


/*
estimates store  TwoStage_QCR_gsample
esttab TwoStage_QCR_gsample using "U:\Research\Testa_SDA Project\Dental Costs\results\TwoStage_QCR_gsample_5Rep.rft", ci stat(N)
*/ 

* step 4 estimate the boostrap SE** 

bstat, stat(beta) n(${Ns}) 
restore