Hi Statalisters,

I am trying to estimate intercept and coefficient of individual level observations using -statsby- command and maximum likelihood OLS regression but hitting a rock. The estimations runs but the estimates are not posted in the new dataset created by statsby. Can someone help with ideas on how to navigate through this problem? Examples are giving below:

Code:
clear
input float(id year weight treat)
 1 0    89.72 1
 1 1   90.177 1
 1 2 93.59666 1
 2 0   84.507 1
 2 1   88.757 1
 2 2   50.763 1
 3 0   73.043 1
 3 1   74.603 1
 3 2   62.313 1
 4 0   82.553 1
 4 1   90.303 1
 4 2   82.823 1
 5 0    96.54 1
 5 1   89.697 1
 5 2   87.447 1
 6 0   29.727 0
 6 1    31.58 0
 6 2   24.757 0
10 0   75.783 1
10 1    78.18 1
10 2   76.493 1
11 0   61.103 0
11 1   50.513 0
11 2   43.983 0
13 0   87.857 1
13 1   87.857 1
13 2   82.923 1
14 0   89.207 1
14 1   95.277 1
14 2   94.837 1
15 0   61.533 0
15 1   73.033 0
15 2   84.683 0
16 0    50.52 0
16 1   68.203 0
16 2   56.113 0
17 0   56.584 0
17 1   31.377 0
17 2   34.297 0
18 0     45.5 0
18 1   77.753 0
18 2   82.347 0
19 0   67.463 0
19 1   84.133 0
19 2   80.003 0
20 0   50.987 0
20 1    70.95 0
20 2    74.63 0
21 0    38.63 0
21 1   18.337 0
21 2   12.173 0
24 0    60.68 0
24 1   82.293 0
24 2   27.597 0
25 0   68.123 0
25 1    53.49 0
25 2     57.6 0
26 0   78.553 1
26 1   88.923 1
26 2   81.813 1
28 0   45.323 0
28 1   60.793 0
28 2   53.777 0
29 0   77.393 0
29 1   77.323 0
29 2   55.083 0
32 0   65.553 0
32 1    64.63 0
32 2   82.133 0
33 0    79.76 1
33 1   81.077 1
33 2    41.76 1
34 0     36.3 0
34 1    56.59 0
34 2   70.257 0
35 0   65.503 0
35 1    47.75 0
35 2   32.507 0
36 0     69.1 1
36 1    65.66 1
36 2    64.85 1
end

Code:
capture program drop lfols
program lfols
  version 14.1
  args lnf xb lnsigma
  local y "$ML_y1"
  quietly replace `lnf' = ln(normalden(`y', `xb',exp(`lnsigma')))
end 

ml model lf lfols (xb: weight = year) (lnsigma:)
statsby cons=_b[xb:_cons] slope= _b[xb:year], by(treat id) clear:  ml maximize

list in 1/10

HTML Code:
treat    id    cons    slope    
                    
1.    0    6    .    .    
2.    0    11    .    .    
3.    0    15    .    .    
4.    0    16    .    .    
5.    0    17    .    .    
                    
6.    0    18    .    .    
7.    0    19    .    .    
8.    0    20    .    .    
9.    0    21    .    .    
10.    0    24    .    .

Any help will be appreciated.

Regards,

Madu