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Stata Technical Bulletin

20.       Morris   683.7607      .0316

21.       Mercer 1228.07       .049

A Simple Bootstrap

example 1. ado performs data resampling, the simplest kind of bootstrap. From an original sample with n cases, we draw
bootstrap samples (also size n) by random sampling with replacement. This is accomplished by letting Stata’s random-number
function uniform () choose the observation numbers (explicit subscripts) of cases included in each bootstrap sample. As written,
examplel. ado executes B=1,000 iterations—adequate for standard-error estimation but probably too few for confidence intervals.
Number of iterations, variable names, and other features can easily be changed or generalized. Comment lines (beginning with
*) briefly explain what the program is doing.

program define examplel

* The first line tells Stata we are going to define a program

* named "example1" . This program bootstraps the mean of a

* variable named "X" , from a dataset called "source.dta" .

* To apply examplel.ado to your own data:
*

*           . use <yourfile.dta>

*           . rename <yourvar> X

*             . keep if X~=.

*            . save source, replace

*
set more 1

*   Tells Stata to wait only 1 second before scrolling a full

*   screen. Default: waits for keyboard input before scrolling,

drop .all
capture erase bootdatl.log
set maxobs 2000

* For confidence intervals or other applications using

* bootstrap-distribution tail percentiles, at least B=2,000

* bootstrap iterations are needed. Simpler purposes, including

* standard error estimation, require substantially fewer
* iterations.

* If source.dta contains > 2,000 cases, set maxobs higher,
log using bootdatl.log
log off

* Log file bootdatl.log will record bootstrap results,
set seed Illl

* Sets the random-generator seed. We can repeat the random
* sequence later by using the same seed, or avoid repeating it
* by choosing a different seed (any large odd number).
macro define .bsample 1

* .bsample counts the number of bootstrap samples.

* .bsample is the name of this macro; ⅝.bsample refers to

* the macro's current contents:
while ⅝.bsample<1001 -(

quietly use source.dta, clear

quietly drop if X==.

quietly generate XX=X[int(.N*uniform())+1]

* Variable XX holds randomly resampled X values. The

* expression int(.N*uniform())+l generates random integers

* from 1 through _N (sample size).

quietly summarize XX
log on

display ⅝.bsample

display .result(3)
display
log off

* For each bootstrap sample, the log file contains the

* sample number and mean of XX.
macro define .bsample=⅝.bsample+l
>

* Curly brackets enclose "while ⅝.bsample<1001" loop,
log close
drop .all
infile bsample bmean using bootdatl.log
label variable bsample "bootstrap sample number"
label variable bmean "sample mean of X"
label data "bootstrap mean"

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