I have written a program which compares the wage distribution for black and
white workers in the U.S.. The program runs fine and I can calculate the
statistics I require. I now want to bootstrap some standard errors. If I
use the basic bootstrap command in Stata 8 :
"bootstrap "program name" statistics, reps(#)
everything is ok and I get my standard errors. However it seems sensible to
ensure that the number of blacks and whites in each bootstrap replication
is consistent with the original data (900 whites and 500 blacks). To
enforce this I tried using the strata option. My new command is therefore
However when I try to run this command the program always fails and I am
told that there are insufficient observations to calculate the standard
errors. The exact message is below
Bootstrap statistics Number of obs = 1370
Number of strata = 2
Replications = 10
------------------------------------------------------------------------------
Variable | Reps Observed Bias Std. Err. [95% Conf. Interval]
-------------+----------------------------------------------------------------
p1 | 0 .6487096 . . . . (N)
| . . (P)
| . . (BC)
------------------------------------------------------------------------------
Note: N = normal
P = percentile
BC = bias-corrected
insufficient observations to compute bootstrap standard errors
no results will be saved
r(2000);
I assume this means there are not enough (more than 1) values of the
statistic generated by the bootstrap command to calculate a standard error.
However I am confused as to why this is so. The program runs o.k with the
original sample sizes so it should also work with each of the 10
bootstrapped samples, resulting in 10 observations. If I don't use the
strata option I get my 10 observations. Any suggestions would be welcomed.
