ello, I have been trying to implement a survey design with the arhomme command, which is a user-generated command that computes a selection QCR. However, the arhomme command does not work with the svyset command, so I am looking for a way to incorporate the survey design to compute proper standard errors.
Below, I have listed the program I have written so far along with the results. I have two questions regarding my approach:
Any guidance on resolving this issue would be greatly appreciated.
Below, I have listed the program I have written so far along with the results. I have two questions regarding my approach:
- Is this an appropriate method for computing the standard errors?
- I encountered an issue when using the n() option in the bstat command. When I tried setting n, I received the following error:
Code:
bstat, stat(beta) n{${N}} option n not allowed
Code:
****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) 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)
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) taupoints(29) rhopoints(35) meshsize(0.5) frank nostderrors centergrid(-0.20)
matrix beta_boot = e(b)
forvalue i = 1/35 {
ereturn scalar beta_boot_`i' =beta_boot[1, `i']
}
**# Bookmark #1
restore
end
* step 3: run the bootstrap**
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_anc_rho = beta_boot[1,35], ///
reps(2) seed(123456): arhomme_bootstrap
* step 4 estimate the boostrap SE**
preserve
bstat, stat(beta) n(${sN})
restore
Bootstrap results Replications = 2
----------------------------------------------------------------------------------------------
| Observed Bootstrap Normal-based
| coefficient std. err. z P>|z| [95% conf. interval]
-----------------------------+----------------------------------------------------------------
r11dentst |
b_r11dentst_dental_insurance | .523821 .0149489 35.04 0.000 .4945217 .5531203
b_r11dentst_endentulism | -.9923927 .0108306 -91.63 0.000 -1.01362 -.971165
b_r11dentst_inc_d | -.1491908 .0453383 -3.29 0.001 -.2380522 -.0603294
b_r11dentst_race | -.08872 .0003113 -285.04 0.000 -.0893301 -.08811
b_r11dentst_age_cat | .08599 .0012628 68.10 0.000 .083515 .088465
b_r11dentst_male | -.2599675 .01408 -18.46 0.000 -.2875639 -.2323711
b_r11dentst_education | .1695978 .0043624 38.88 0.000 .1610477 .1781479
b_r11dentst_veteran | .0551342 .0209855 2.63 0.009 .0140035 .096265
b_r11dentst_mothered | .0268983 .0178833 1.50 0.133 -.0081524 .0619489
b_r11dentst_wealth | .3405195 .0122915 27.70 0.000 .3164285 .3646104
b_r11dentst_smoke_now | -.2529702 .0224962 -11.25 0.000 -.2970619 -.2088786
b_r11dentst_chronicdisease | -.0131275 .0062461 -2.10 0.036 -.0253697 -.0008854
b_r11dentst_cons | -1.068637 .0612556 -17.45 0.000 -1.188696 -.9485786
-----------------------------+----------------------------------------------------------------
.1_quantile |
b_q10_cons | 1.862657 .4849658 3.84 0.000 .9121417 2.813173
b_q10_inc_d | -.1892664 .206257 -0.92 0.359 -.5935228 .2149899
b_q10_endentulism | -.8884163 .0183617 -48.38 0.000 -.9244046 -.8524279
b_q10_race_2 | -.3892348 .2454278 -1.59 0.113 -.8702643 .0917948
b_q10_race_3 | -.0540006 .2649967 -0.20 0.839 -.5733846 .4653833
b_q10_race_4 | -.5208147 .0819425 -6.36 0.000 -.6814189 -.3602104
b_q10_age_cat_2 | .2948959 .1437055 2.05 0.040 .0132383 .5765535
b_q10_age_cat_3 | .7219095 .049809 14.49 0.000 .6242856 .8195334
b_q10_age_cat_4 | .6959584 .0175763 39.60 0.000 .6615094 .7304074
b_q10_male | -.3594654 .0454797 -7.90 0.000 -.4486039 -.2703268
b_q10_education_2 | .3748066 .4328375 0.87 0.387 -.4735393 1.223153
b_q10_education_3 | .7279758 .4908594 1.48 0.138 -.234091 1.690043
b_q10_education_4 | .9618373 .432749 2.22 0.026 .1136648 1.81001
b_q10_education_5 | 1.219666 .4589004 2.66 0.008 .3202383 2.119095
b_q10_veteran | .1031842 .0408082 2.53 0.011 .0232016 .1831668
b_q10_mothered | -.0244031 .0041735 -5.85 0.000 -.0325829 -.0162232
b_q10_wealth_2 | .5151052 .2581096 2.00 0.046 .0092197 1.020991
b_q10_wealth_3 | .8368382 .0887208 9.43 0.000 .6629487 1.010728
b_q10_wealth_4 | 1.056053 .021892 48.24 0.000 1.013145 1.09896
b_q10_smoke_now | -.1627762 .1467932 -1.11 0.267 -.4504856 .1249331
b_q10_chronicdisease | .0370667 .0117938 3.14 0.002 .0139512 .0601822
-----------------------------+----------------------------------------------------------------
_anc |
b_anc_rho | -5.915026 . . . . .
----------------------------------------------------------------------------------------------
