st: Re: Bootstrap error message

[Alistair Windsor <alistair.windsor@gmail.com>]

Dear All,

A follow up on how to avoid my mysterious error message and also how to 
avoid bootstrap from rejecting runs with different variables dropped for 
collinearity (if such is a good idea). The solution is to insert

ereturn clear

into the program before returning to the bootstrap. This prevents any of 
it's "helpful" checks from throwing an exception.

If one allows the regressions to drop different variables when computing 
the pscore then one probably should return the number of observations to 
the boostrap. One can use the reject option to eliminate runs where the 
number of propensity scores computed is lower than it should be.

Alistair


On 5/11/11 9:50 AM, Alistair Windsor wrote:
> 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,
>
> Alistair
>
> 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'
> }
>
> end
>
> 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
>
> pause
>
> 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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