Dear Members,

I am dealing with DHS dataset. I have individual sample weight (6 decimals) in my dataset. I have divide it to 1000000. I named it as"wgt"
I try to produce sum stats. When I do this, I use sum x1 x2 x3 .... [aw=wgt]. No problem with that. It gives mt stats.
However when I try to export it to MS word, I use asdoc sum x1 x2....[aw=wgt] it says "aweights" are not allowed. As far as I know, when reporting descriptive stats, you need to use aweights. But, I dont understand why I could not export them

This code works so well: sum bfduration2 survey_year female child_birth_year child_birth_month birthweight mother_age wealth_index rural_urban region5 [aw=wgt]
But this does not: asdoc sum bfduration2 survey_year female child_birth_year child_birth_month birthweight mother_age wealth_index rural_urban region5 [aw=wgt]

Thank you so much.

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(bfduration2 survey_year female child_birth_year child_birth_month birthweight mother_age wealth_index rural_urban region5) double wgt
30 2018 1 2014  9   3.2 28 4 1 1 2.356055974960327
12 2018 1 2016  1  3.56 30 5 1 1 2.356055974960327
 8 2018 0 2018  2  3.95 29 5 1 1 2.356055974960327
18 2018 0 2014  2   3.6 35 5 1 1 2.356055974960327
19 2018 1 2014  3   2.1 30 4 1 1 2.356055974960327
18 2018 0 2014  4   3.3 41 3 2 1 2.444962978363037
18 2018 1 2014  2   2.5 35 1 2 1 2.444962978363037
 3 2018 1 2015  6   3.5 26 1 2 1 2.444962978363037
10 2018 1 2016  8     . 43 1 2 1 2.444962978363037
 2 2018 1 2018  8  2.77 32 2 2 1 2.444962978363037
36 2018 1 2014 10   4.2 37 2 2 1 2.444962978363037
18 2018 1 2013 11   3.4 23 3 2 1 2.444962978363037
24 2018 0 2015  7  3.26 42 3 1 1 2.356055974960327
24 2018 0 2016 10   4.8 22 5 1 1 2.356055974960327
 8 2018 0 2014  4   3.2 28 1 1 1 2.356055974960327
 1 2018 0 2018  9  2.78 32 3 1 1 2.356055974960327
20 2018 1 2016  3  3.64 44 5 1 1 2.356055974960327
10 2018 1 2017 12  4.23 31 4 1 1 2.356055974960327
 7 2018 0 2016 11   3.8 32 5 1 1 2.356055974960327
24 2018 1 2013 12  3.14 35 5 1 1 2.356055974960327
 6 2018 1 2018  4   2.9 24 3 1 1 2.356055974960327
14 2018 1 2017  8     3 29 2 1 1 2.356055974960327
27 2018 0 2015  9  4.08 29 4 1 1 2.356055974960327
26 2018 0 2016  9  3.48 35 5 1 1 2.356055974960327
18 2018 1 2015  7   3.1 26 2 1 1 2.356055974960327
 9 2018 1 2017  4     3 33 2 1 1 2.356055974960327
15 2018 1 2017  2   3.6 36 5 1 1 2.356055974960327
21 2018 1 2017  1   4.1 26 5 1 1 2.356055974960327
13 2018 1 2017  9   2.9 32 4 1 1 2.356055974960327
13 2018 1 2017 10   3.9 34 5 1 1 2.356055974960327
35 2018 1 2015 12  3.01 32 5 1 1 2.356055974960327
 6 2018 1 2018  1 3.295 33 3 1 1 2.356055974960327
 3 2018 1 2018  8   3.5 27 4 1 1 2.356055974960327
 2 2018 1 2014  1   3.5 41 4 1 1 2.356055974960327
 7 2018 0 2017  5  3.43 41 4 2 1 2.378074884414673
21 2018 1 2014  6  3.34 28 2 2 1 2.378074884414673
 8 2018 1 2018  2  2.26 35 3 2 1 2.378074884414673
 1 2018 0 2017 12   3.5 19 1 1 1 2.356055974960327
 5 2018 1 2018  5  2.99 23 1 1 1 2.356055974960327
12 2018 0 2017 10  3.68 22 1 1 1 2.356055974960327
12 2018 1 2017  3   3.5 26 5 1 1 2.356055974960327
18 2018 1 2016  7     4 22 1 1 1 2.356055974960327
10 2018 1 2015  1   3.5 35 5 1 1 2.356055974960327
36 2018 0 2014  6  3.06 41 5 1 1 2.356055974960327
18 2018 0 2014  8   3.6 37 5 1 1 2.356055974960327
 4 2018 1 2018  5  3.46 33 5 1 1 2.356055974960327
 3 2018 1 2018  7   3.2 35 5 1 1 2.356055974960327
10 2018 1 2018  1  3.27 21 4 1 1 2.356055974960327
 5 2018 0 2018  3  3.16 21 4 1 1 2.356055974960327
24 2018 0 2013 12  3.26 45 4 1 1 2.356055974960327
46 2018 1 2015  1     3 33 3 1 1 2.356055974960327
 2 2018 0 2014 12   3.4 25 4 1 1 2.356055974960327
24 2018 1 2015  5   2.9 36 5 1 1 2.356055974960327
20 2018 0 2016  6   3.7 31 2 1 1 2.356055974960327
22 2018 0 2016 12  3.25 33 5 1 1 2.356055974960327
 3 2018 0 2018  8   2.5 33 5 1 1 2.356055974960327
 6 2018 0 2015 11   3.7 29 5 1 1 2.356055974960327
 4 2018 1 2015 11  2.68 25 4 1 1 2.356055974960327
24 2018 1 2013 11   3.3 38 3 1 1 2.356055974960327
18 2018 0 2015 10   4.2 29 4 1 1 2.356055974960327
 5 2018 1 2018  4   3.5 25 4 1 1 2.356055974960327
 5 2018 0 2017  9  3.75 24 4 1 1 2.356055974960327
 1 2018 1 2018 10   3.7 30 3 1 1 2.356055974960327
12 2018 0 2015  1     . 28 2 1 1 2.356055974960327
24 2018 0 2015  5   3.6 35 5 1 1 2.356055974960327
11 2018 1 2015 11   3.8 29 3 1 1 2.356055974960327
 8 2018 1 2018  2  3.65 31 3 1 1 2.356055974960327
20 2018 0 2017  2     . 24 1 1 1 2.356055974960327
24 2018 1 2013 12   2.9 30 5 1 1 2.356055974960327
 6 2018 1 2018  5   3.3 23 4 1 1 2.356055974960327
18 2018 0 2014 10   2.7 32 3 1 1 2.356055974960327
24 2018 1 2015  9   2.9 28 5 1 1 2.356055974960327
26 2018 1 2016  8   3.5 26 2 1 1 2.356055974960327
18 2018 0 2015 11  2.45 23 3 1 1 2.356055974960327
 8 2018 1 2018  2   3.5 28 3 1 1 2.356055974960327
18 2018 0 2015  7   3.2 38 3 1 1 2.356055974960327
 2 2018 1 2016  8  2.85 21 2 1 1 2.356055974960327
10 2018 1 2017 11   2.5 28 3 1 1 2.356055974960327
 6 2018 0 2015 12   2.3 34 3 1 1 2.356055974960327
 2 2018 1 2018  7  3.35 40 5 1 1 2.356055974960327
18 2018 0 2017  4   2.9 35 5 1 1 2.356055974960327
24 2018 0 2014  4  2.65 33 5 1 1 2.356055974960327
 7 2018 0 2017  1     3 37 5 1 1 2.356055974960327
 4 2018 0 2018  6   3.9 34 5 1 1 2.356055974960327
20 2018 1 2016  6   2.5 32 2 1 1 2.356055974960327
26 2018 0 2016  8   3.1 37 3 1 1 2.356055974960327
24 2018 0 2014  3  3.15 38 4 1 1 2.356055974960327
 6 2018 0 2016 11  2.45 23 3 1 1 2.356055974960327
 2 2018 0 2018  7  2.65 37 4 1 1 2.356055974960327
24 2018 1 2016  4   4.5 41 5 1 1 2.356055974960327
 1 2018 0 2018  9   3.2 37 4 1 1 2.356055974960327
 7 2018 0 2018  3   3.1 28 4 1 1 2.356055974960327
29 2018 0 2016  5  2.98 26 3 1 1 2.356055974960327
12 2018 0 2016  5     . 25 3 1 1 2.356055974960327
27 2018 1 2014  1   3.2 35 5 1 1 2.356055974960327
24 2018 1 2014  4   3.2 41 3 1 1 2.356055974960327
24 2018 1 2016  1  3.09 25 3 1 1 2.356055974960327
30 2018 0 2015  6   3.9 31 5 1 1 2.356055974960327
 6 2018 1 2014  8   3.5 40 5 1 1 2.356055974960327
 1 2018 0 2018 10   2.4 18 2 1 1 2.356055974960327
end
label values wealth_index wealth_index
label def wealth_index 1 "Poorest", modify
label def wealth_index 2 "Poorer", modify
label def wealth_index 3 "Middle", modify
label def wealth_index 4 "Rich", modify
label def wealth_index 5 "Richest", modify
label values rural_urban rural_urban
label def rural_urban 1 "Urban", modify
label def rural_urban 2 "Rural", modify
label values region5 region5
label def region5 1 " West", modify