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st: Bootstrap error message

From   Alistair Windsor <>
Subject   st: Bootstrap error message
Date   Wed, 11 May 2011 09:50:38 -0500

Dear Statalisters,

I am using a propensity score matching scheme to evaluate an education intervention. Students self select into the intervention so some effort needs to be taken to eliminate the selection bias in the data.

I am aware of the Imbens result on the lack of asymptotic validity for bootstrapping for matching schemes and I am trying some propensity score reweighting schemes as well but thusfar the propensity score matching scheme does the best job of eliminating observed difference and seems to be the least sensitive to specification. In addition it is easy to explain.

My problem concerns the fact that when I run my bootstrap command over my matching scheme in a case where there is classification with no students in it I get an error message

e(b) not found

and the bootstrap aborts. Altering the code so that it should adapt to empty classifications has not helped. The relevant code in enclosed in a capture block. It all ran fine until I turned it into a program and bootstrapped it. Can anyone see my mistake?

Many thanks in advance,


My program is as follows:

capture program drop difference
program define difference, rclass
local intervention  `1'
local controls female i.race i.major freshgpa nfreshgpa
local outcomes retained gpa

qui: gen float weight = .
foreach measure of local outcomes {
  qui: gen att_`measure' = .

forvalues y = 1/2 {
qui: levelsof classification if `intervention'==1 & yr==`y', local(classifications)
  foreach c of local classifications {
    capture {
      qui: xi: psmatch2 `intervention' `controls' ///
        if yr  == `y' & classification == "`c'", ///
outcome(`outcomes') noreplacement descending common quietly nowarning
      qui: replace weight = _weight if yr == `y' & classification == "`c'"
      foreach measure of local outcomes {
        qui: replace att_`measure' = r(att_`measure') if yr == `y' & ///
          classification == "`c'" & `intervention' & weight == 1
sum att_gpa if `intervention' == 1 & weight == 1, meanonly
return scalar gpa_diff = r(mean)
sum att_retained if `intervention' == 1 & weight == 1, meanonly
return scalar retained_diff = r(mean)
qui: drop weight
foreach measure of local outcomes {
    qui: drop att_`measure'


The main body of the loop

foreach intervention of local interventions {

  log using ...

  qui: drop if mstem & `intervention' == 0

bootstrap r(gpa_diff) r(retained_diff), seed(6784431) strata(`intervention' yr) reps(15) nodrop : difference `intervention'

  log close


restore, preserve // This restores the data set to where it was before I dropped students.


It took a while for me to discover the nodrop option. The code is a bastardized mix of Stata 10 and 11. Apologies.
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