Dear All,
I am trying to estimate the effect of having COVID-19 on subsequently receiving a chronic pain diagnosis. Since, death is a competing risk (if you die after COVID you cannot receive a chronic pain dx), I am estimating a competing risk survival model for duration until receipt of a new chronic pain diagnosis conditional upon not having received one yet and not dying. To control for potential endogeneity of having COVID, I am estimating a two part model as suggested in https://jhr.uwpress.org/content/50/2/420. I want to bootstrap my standard errors for a two step estimation procedure but am getting an error message. Here is the dataex for my data:
When I run the two step procedure without the bootstrapping it runs fine:
But, when I run this in a bootstrap program I get an error:
I seem to have plenty of observations... so not sure what is going on. I will be very grateful for any advice you may be able to offer to resolve the issue.
Many thanks,
Sumedha
I am trying to estimate the effect of having COVID-19 on subsequently receiving a chronic pain diagnosis. Since, death is a competing risk (if you die after COVID you cannot receive a chronic pain dx), I am estimating a competing risk survival model for duration until receipt of a new chronic pain diagnosis conditional upon not having received one yet and not dying. To control for potential endogeneity of having COVID, I am estimating a two part model as suggested in https://jhr.uwpress.org/content/50/2/420. I want to bootstrap my standard errors for a two step estimation procedure but am getting an error message. Here is the dataex for my data:
Code:
* Example generated by -dataex-. For more info, type help dataex clear input float grpatidtreat byte(Female age80plus Asian Black Hispanic) float COVID double PopDensity float tavg double prcp byte(_st _d _t _t0) 15206 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 3004 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 17435 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 2112 1 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 26240 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 19463 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 20156 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 1578 1 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 20172 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 19990 0 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 24292 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 22231 1 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 1 1 0 3816 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 1326 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 25147 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 24294 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 6048 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 20208 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 12468 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 24929 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 18131 0 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 8075 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 18614 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 1100 1 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 20014 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 20349 1 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 15241 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 8237 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 347 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 532 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 20100 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 7709 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 21965 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 245 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 12819 0 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 26574 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 22608 1 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 20670 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 10468 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 2431 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 22729 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 19918 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 6767 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 2013 1 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 13247 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 409 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 24019 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 8130 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 1503 1 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 450 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 25712 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 21945 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 7870 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 19688 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 22838 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 26055 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 6305 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 2688 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 7771 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 21690 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 24805 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 22742 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 5887 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 14911 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 10076 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 19660 1 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 23040 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 1188 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 18448 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 7966 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 2058 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 693 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 338 0 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 1245 0 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 25259 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 22337 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 21719 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 21801 0 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 19906 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 2593 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 10784 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 7057 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 22519 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 1 1 0 22109 0 0 0 0 1 0 99.2 26.89387 102.04999969899654 1 0 1 0 1508 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 24739 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 19928 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 22893 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 19535 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 22551 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 10009 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 1990 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 14715 1 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 8545 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 24844 0 0 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 20016 1 1 0 0 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 21723 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 20133 0 1 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 7299 1 0 0 1 0 0 99.2 26.89387 102.04999969899654 1 0 1 0 24604 0 0 0 0 1 0 99.2 26.89387 102.04999969899654 1 0 1 0 end label values COVID COVID label def COVID 0 "No confirmed COVID-19", modify
Code:
. probit COVID Female age80plus Asian Black Hispanic PopDensity tavg prcp if sample==1
Iteration 0: Log likelihood = -84773.888
Iteration 1: Log likelihood = -84145.633
Iteration 2: Log likelihood = -84139.719
Iteration 3: Log likelihood = -84139.718
Probit regression Number of obs = 697,196
LR chi2(8) = 1268.34
Prob > chi2 = 0.0000
Log likelihood = -84139.718 Pseudo R2 = 0.0075
------------------------------------------------------------------------------
COVID | Coefficient Std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
Female | .024962 .006397 3.90 0.000 .012424 .0375
age80plus | -.1059373 .0076874 -13.78 0.000 -.1210044 -.0908702
Asian | .0091485 .0177039 0.52 0.605 -.0255504 .0438474
Black | .0673043 .0105328 6.39 0.000 .0466603 .0879483
Hispanic | .2271459 .0092874 24.46 0.000 .2089429 .245349
PopDensity | .0001692 .0000121 14.04 0.000 .0001455 .0001928
tavg | -.0063606 .0003474 -18.31 0.000 -.0070415 -.0056797
prcp | .0001733 .0000623 2.78 0.005 .0000512 .0002954
_cons | -1.933265 .0082191 -235.22 0.000 -1.949374 -1.917156
------------------------------------------------------------------------------
.
. //margins, dydx(*)
. predict xd2h, index
. gen phi2h = normalden(xd2h)
. gen PHI2h = normal(xd2h)
. gen gr2 = COVID*phi2h/PHI2h - (1 - COVID)*phi2h/(1 - PHI2h)
. gen COVID_gr2 = COVID*gr2
.
.
. foreach v in Female age80plus Asian Black Hispanic {
2. sum `v'
3. gen `v'm=`v'-r(mean)
4. gen COVID_`v'm=COVID*`v'm
5. gen COVID`v'gr2=COVID_`v'm*gr2
6. }
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
Female | 697,605 .5619885 .4961429 0 1
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
age80plus | 697,605 .2472932 .4314389 0 1
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
Asian | 697,605 .0342801 .181948 0 1
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
Black | 697,605 .0990632 .2987471 0 1
Variable | Obs Mean Std. dev. Min Max
-------------+---------------------------------------------------------
Hispanic | 697,605 .1089069 .3115226 0 1
.
. ***Second Stage***
. eststo: stcrreg Female age80plus Asian Black Hispanic COVID gr2 if sample==1, compete(d2=2)
Failure _d: d2==1
Analysis time _t: stop
Enter on or after: time start
ID variable: grpatidtreat
Iteration 0: Log pseudolikelihood = -37079.611
Iteration 1: Log pseudolikelihood = -37077.898
Iteration 2: Log pseudolikelihood = -37077.897
Competing-risks regression No. of obs = 697,196
No. of subjects = 25,644
Failure event: d2 == 1 No. failed = 3,737
Competing event: d2 == 2 No. competing = 2,261
No. censored = 19,646
Wald chi2(7) = 1227.46
Log pseudolikelihood = -37077.897 Prob > chi2 = 0.0000
(Std. err. adjusted for 25,644 clusters in grpatidtreat)
------------------------------------------------------------------------------
| Robust
_t | SHR std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
Female | 1.095094 .0366405 2.71 0.007 1.025584 1.169315
age80plus | 3.00898 .1062511 31.20 0.000 2.807775 3.224604
Asian | .9216742 .0904136 -0.83 0.406 .7604611 1.117063
Black | 1.128179 .0618504 2.20 0.028 1.01324 1.256156
Hispanic | 1.237449 .0724726 3.64 0.000 1.103255 1.387966
COVID | .0949024 .1989416 -1.12 0.261 .0015593 5.776043
gr2 | 3.251186 2.834039 1.35 0.176 .5889177 17.94854
------------------------------------------------------------------------------
(est47 stored)
. estimates store ProbitCF
. //stcurve, cif at(COVID=(0 1)) legend(label(1 "W/o COVID-19 infection") label(2 "With COVID-19 infection") pos(6) cols(2)) lcolor(navy khaki) name(yes2sriprobit
> 1b)
.
. eststo: stcrreg Female age80plus Asian Black Hispanic COVID gr2 Femalem age80plusm Asianm Blackm Hispanicm if sample==1, compete(d2=2)
note: Femalem omitted because of collinearity.
note: age80plusm omitted because of collinearity.
note: Asianm omitted because of collinearity.
note: Blackm omitted because of collinearity.
note: Hispanicm omitted because of collinearity.
Failure _d: d2==1
Analysis time _t: stop
Enter on or after: time start
ID variable: grpatidtreat
Iteration 0: Log pseudolikelihood = -37079.611
Iteration 1: Log pseudolikelihood = -37077.898
Iteration 2: Log pseudolikelihood = -37077.897
Competing-risks regression No. of obs = 697,196
No. of subjects = 25,644
Failure event: d2 == 1 No. failed = 3,737
Competing event: d2 == 2 No. competing = 2,261
No. censored = 19,646
Wald chi2(7) = 1227.46
Log pseudolikelihood = -37077.897 Prob > chi2 = 0.0000
(Std. err. adjusted for 25,644 clusters in grpatidtreat)
------------------------------------------------------------------------------
| Robust
_t | SHR std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
Female | 1.095094 .0366405 2.71 0.007 1.025584 1.169315
age80plus | 3.00898 .1062511 31.20 0.000 2.807775 3.224604
Asian | .9216742 .0904136 -0.83 0.406 .7604611 1.117063
Black | 1.128179 .0618504 2.20 0.028 1.01324 1.256156
Hispanic | 1.237449 .0724726 3.64 0.000 1.103255 1.387966
COVID | .0949024 .1989416 -1.12 0.261 .0015593 5.776043
gr2 | 3.251186 2.834039 1.35 0.176 .5889177 17.94854
Femalem | 1 (omitted)
age80plusm | 1 (omitted)
Asianm | 1 (omitted)
Blackm | 1 (omitted)
Hispanicm | 1 (omitted)
------------------------------------------------------------------------------
(est48 stored)
. estimates store ProbitCFm
. //stcurve, cif at(COVID=(0 1)) legend(label(1 "W/o COVID-19 infection") label(2 "With COVID-19 infection") pos(6) cols(2)) lcolor(navy khaki) name(yes2sriprobit
> 1b)
.
. eststo: stcrreg Female age80plus Asian Black Hispanic COVID gr2 Femalem age80plusm Asianm Blackm Hispanicm COVID_gr2 if sample==1, compete(d2=2)
note: Femalem omitted because of collinearity.
note: age80plusm omitted because of collinearity.
note: Asianm omitted because of collinearity.
note: Blackm omitted because of collinearity.
note: Hispanicm omitted because of collinearity.
Failure _d: d2==1
Analysis time _t: stop
Enter on or after: time start
ID variable: grpatidtreat
Iteration 0: Log pseudolikelihood = -37078.386
Iteration 1: Log pseudolikelihood = -37076.647
Iteration 2: Log pseudolikelihood = -37076.647
Competing-risks regression No. of obs = 697,196
No. of subjects = 25,644
Failure event: d2 == 1 No. failed = 3,737
Competing event: d2 == 2 No. competing = 2,261
No. censored = 19,646
Wald chi2(8) = 1228.54
Log pseudolikelihood = -37076.647 Prob > chi2 = 0.0000
(Std. err. adjusted for 25,644 clusters in grpatidtreat)
------------------------------------------------------------------------------
| Robust
_t | SHR std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
Female | 1.105049 .0375456 2.94 0.003 1.033857 1.181142
age80plus | 2.894589 .1248682 24.64 0.000 2.659913 3.14997
Asian | .926506 .090886 -0.78 0.436 .7644503 1.122916
Black | 1.153697 .065492 2.52 0.012 1.032218 1.289472
Hispanic | 1.350739 .1071294 3.79 0.000 1.156275 1.577908
COVID | .1217421 .248928 -1.03 0.303 .002213 6.697409
gr2 | 92.16569 210.356 1.98 0.047 1.051512 8078.382
Femalem | 1 (omitted)
age80plusm | 1 (omitted)
Asianm | 1 (omitted)
Blackm | 1 (omitted)
Hispanicm | 1 (omitted)
COVID_gr2 | .0290399 .0640659 -1.60 0.109 .0003847 2.192102
------------------------------------------------------------------------------
(est49 stored)
. estimates store ProbitCFm_COVID_gr2
. //stcurve, cif at(COVID=(0 1)) legend(label(1 "W/o COVID-19 infection") label(2 "With COVID-19 infection") pos(6) cols(2)) lcolor(navy khaki) name(yes2sriprobit
> 1b)
.
. eststo: stcrreg Female age80plus Asian Black Hispanic COVID gr2 Femalem age80plusm Asianm Blackm Hispanicm COVID_gr2 COVIDFemalegr2 COVIDage80plusgr2 COVIDAsian
> gr2 COVIDBlackgr2 COVIDHispanicgr2 if sample==1, compete(d2=2)
note: Femalem omitted because of collinearity.
note: age80plusm omitted because of collinearity.
note: Asianm omitted because of collinearity.
note: Blackm omitted because of collinearity.
note: Hispanicm omitted because of collinearity.
Failure _d: d2==1
Analysis time _t: stop
Enter on or after: time start
ID variable: grpatidtreat
Iteration 0: Log pseudolikelihood = -37073.45
Iteration 1: Log pseudolikelihood = -37071.665
Iteration 2: Log pseudolikelihood = -37071.665
Competing-risks regression No. of obs = 697,196
No. of subjects = 25,644
Failure event: d2 == 1 No. failed = 3,737
Competing event: d2 == 2 No. competing = 2,261
No. censored = 19,646
Wald chi2(13) = 1234.21
Log pseudolikelihood = -37071.665 Prob > chi2 = 0.0000
(Std. err. adjusted for 25,644 clusters in grpatidtreat)
-----------------------------------------------------------------------------------
| Robust
_t | SHR std. err. z P>|z| [95% conf. interval]
------------------+----------------------------------------------------------------
Female | 1.096771 .0378428 2.68 0.007 1.025053 1.173507
age80plus | 2.913917 .1276074 24.42 0.000 2.674244 3.17507
Asian | .9346944 .0924489 -0.68 0.495 .769979 1.134646
Black | 1.131962 .0659494 2.13 0.033 1.009811 1.26889
Hispanic | 1.372199 .1106521 3.92 0.000 1.171595 1.607152
COVID | .1225937 .4014966 -0.64 0.522 .0001999 75.19144
gr2 | 99.51189 229.6945 1.99 0.046 1.079248 9175.477
Femalem | 1 (omitted)
age80plusm | 1 (omitted)
Asianm | 1 (omitted)
Blackm | 1 (omitted)
Hispanicm | 1 (omitted)
COVID_gr2 | .0270263 .0711116 -1.37 0.170 .0001556 4.693214
COVIDFemalegr2 | 1.110252 .0937759 1.24 0.216 .9408619 1.310138
COVIDage80plusgr2 | .8821976 .0889958 -1.24 0.214 .7239306 1.075065
COVIDAsiangr2 | .8376026 .2649553 -0.56 0.575 .4505895 1.557023
COVIDBlackgr2 | 1.206487 .1397693 1.62 0.105 .9614187 1.514024
COVIDHispanicgr2 | .8214927 .1474551 -1.10 0.273 .5778511 1.167862
-----------------------------------------------------------------------------------
(est50 stored)
Code:
. ***First Stage***
. probit COVID Female age80plus Asian Black Hispanic PopDensity tavg prcp if sa
> mple==1
2. predict xd2h, index
3. gen phi2h = normalden(xd2h)
4. gen PHI2h = normal(xd2h)
5. gen gr2 = COVID*phi2h/PHI2h - (1 - COVID)*phi2h/(1 - PHI2h)
6. gen COVID_gr2 = COVID*gr2
7.
. foreach v in Female age80plus Asian Black Hispanic {
8. sum `v'
9. gen `v'm=`v'-r(mean)
10. gen COVID_`v'm=COVID*`v'm
11. gen COVID`v'gr2=COVID_`v'm*gr2
12. }
13.
. ***Second Stage***
. eststo: stcrreg Female age80plus Asian Black Hispanic COVID gr2 if sample==1,
> compete(d2=2)
14.
. eststo: stcrreg Female age80plus Asian Black Hispanic COVID gr2 Femalem age80
> plusm Asianm Blackm Hispanicm if sample==1, compete(d2=2)
15.
. eststo: stcrreg Female age80plus Asian Black Hispanic COVID gr2 Femalem age80
> plusm Asianm Blackm Hispanicm COVID_gr2 if sample==1, compete(d2=2)
16.
. eststo: stcrreg Female age80plus Asian Black Hispanic COVID gr2 Femalem age80
> plusm Asianm Blackm Hispanicm COVID_gr2 COVIDFemalegr2 COVIDage80plusgr2 COVI
> DAsiangr2 COVIDBlackgr2 COVIDHispanicgr2 if sample==1, compete(d2=2)
17.
. drop xd2h phi2h PHI2h gr2 Femalem age80plusm Asianm Blackm Hispanicm COVID_gr
> 2 COVIDFemalegr2 COVIDage80plusgr2 COVIDAsiangr2 COVIDBlackgr2 COVIDHispanicg
> r2
18.
. end
.
. bootstrap _b, reps(100) seed(123): boot
(running boot on estimation sample)
Bootstrap replications (100): xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx
> xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx done
x: Error occurred when bootstrap executed boot.
insufficient observations to compute bootstrap standard errors
no results will be saved
r(2000);
end of do-file
r(2000);
Many thanks,
Sumedha
