Hi,
My actual dataset includes my quantities of interest (A, B, C, and D) obtained from 1000 bootstrap iterations by estimation type (unweighted vs. weighted) as well as profile (W and B). The below is a fraction of my actual dataset by showing the first 10 iterations by estimation and profile variables.

I would like to obtain and store the 95% confidence interval overlap by estimation and profile variables separately for my quantities of interest (i.e., A, B, C, D). The below centile command returns me the 95% confidence intervals for A, B, C, and D but I wonder if there is a way to obtain and store the 95% confidence interval overlaps as well.
Thanks,
NM

bysort estimation profile_new: centile TLE HLE ULE PLE, centile(2.5 97.5)
Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input str26 estimation float iteration str10 profile_new float(A B C D)
"Unweighted"  1 "0.W"  31.22547  23.49367  7.731804 .7523879
"Unweighted"  2 "0.W" 31.419674 23.715366  7.704307 .7547935
"Unweighted"  3 "0.W" 31.432106  23.62115  7.810956 .7514975
"Unweighted"  4 "0.W"  31.53055  23.81336  7.717191 .7552472
"Unweighted"  5 "0.W" 30.975897  23.23714  7.738756 .7501684
"Unweighted"  6 "0.W" 30.924076  23.21421  7.709865  .750684
"Unweighted"  7 "0.W" 31.097807  23.40726  7.690547 .7526981
"Unweighted"  8 "0.W" 31.188095  23.28139  7.906707 .7464831
"Unweighted"  9 "0.W"  31.04022  23.28384  7.756382 .7501184
"Unweighted" 10 "0.W" 31.259636  23.34705  7.912586 .7468753
"Unweighted"  1 "1.B"  28.60185 18.081507 10.520342 .6321797
"Unweighted"  2 "1.B"  28.84507 18.180395 10.664673 .6302774
"Unweighted"  3 "1.B"   28.5759 17.774813 10.801084 .6220211
"Unweighted"  4 "1.B" 29.137196 17.764761 11.372436 .6096936
"Unweighted"  5 "1.B" 28.944084  17.77063 11.173453 .6139641
"Unweighted"  6 "1.B" 28.396053 17.762722  10.63333 .6255349
"Unweighted"  7 "1.B"  28.90983  17.92432 10.985507 .6200079
"Unweighted"  8 "1.B" 27.772045 17.330206  10.44184 .6240162
"Unweighted"  9 "1.B"  28.76616 18.037151 10.729006 .6270268
"Unweighted" 10 "1.B"  28.88433 18.255674 10.628655 .6320269
"Weighted"    1 "0.W" 31.242056 23.867405  7.374651 .7639512
"Weighted"    2 "0.W"  31.61572  23.85055  7.765174 .7543889
"Weighted"    3 "0.W"  31.22626  23.84484  7.381419  .763615
"Weighted"    4 "0.W"  31.38188 23.941223  7.440657 .7628996
"Weighted"    5 "0.W"  31.76251  24.47861  7.283905  .770676
"Weighted"    6 "0.W"  31.60152  23.86003  7.741492 .7550279
"Weighted"    7 "0.W"  31.68685  24.23607  7.450778 .7648621
"Weighted"    8 "0.W" 31.084545 23.783575  7.300969 .7651254
"Weighted"    9 "0.W"  31.06961 23.824186  7.245427 .7668002
"Weighted"   10 "0.W" 31.539604  24.01042  7.529183 .7612785
"Weighted"    1 "1.B"   28.2727  18.17689  10.09581 .6429131
"Weighted"    2 "1.B"  28.70915 18.328226 10.380924 .6384106
"Weighted"    3 "1.B" 27.879877  18.14567  9.734207 .6508518
"Weighted"    4 "1.B" 29.038834 18.873323  10.16551 .6499339
"Weighted"    5 "1.B"   28.3958 18.071054 10.324748 .6363988
"Weighted"    6 "1.B"  29.26605 19.116856 10.149196 .6532092
"Weighted"    7 "1.B"  28.50781  18.73163  9.776183   .65707
"Weighted"    8 "1.B"  28.23492 18.348585  9.886334 .6498543
"Weighted"    9 "1.B"  28.42887 18.459541   9.96933 .6493238
"Weighted"   10 "1.B"  28.82799 18.732927  10.09506 .6498173
end