Stata results do not display se and pvalue

[Karen Jyo]

Dear all, I am currently using ppml for gravity estimates. The aim is to estimate the impact of NTMs on seafood export of one country. (136 destinations and 15years)
The dependent variable is trade value, the key variable is NTM quantity, and the control variables are lnPOP, lnGDP, lnDistance, and so on.

In group estimates at HS 4 level, I control for country FE and year FE. (136 destinations and 15years, 2040 obs)
However, the results of one group of Group A (HS 0301) did not give se, p-value and other values. The remaining groups were normal.

After I use ppmlhdfe or drop control variables, the results of Group A include se, p-value, and other numerical values.

However, I don't know what the correct solution to this problem is.
I hope someone can tell me how to handle this situation (use ppmlhdfe, drop control vars, or sth else) and how to report it in the paper. Thanks.

Here is the Stata coding & Group A result:

__________________________________________________ __
egen code_d=group(iso3num_d)
tabulate code_d,generate(d_fe)
egen code_year=group(year)
tabulate code_year,generate(year_fe)
global fe d_fe* year_fe*

ppml value0301 $fe lndist lnpop_d lngdp_d NTM_0301

note: checking the existence of the estimates
WARNING: value0301 has very large values, consider rescaling
WARNING: lnpop_d has very large values, consider rescaling or recentering
WARNING: lngdp_d has very large values, consider rescaling or recentering

Number of regressors excluded to ensure that the estimates exist: 58
Excluded regressors: d_fe1 d_fe8 d_fe11 d_fe12 d_fe14 d_fe15 d_fe16 d_fe17 d_fe21 d_fe25 d_fe27 d_fe28 d_fe29 d_fe30 d_fe31 d_fe32 d_f
> e37 d_fe38 d_fe39 d_fe40 d_fe41 d_fe42 d_fe43 d_fe44 d_fe46 d_fe50 d_fe51 d_fe52 d_fe53 d_fe56 d_fe57 d_fe63 d_fe64 d_fe73 d_fe74 d_f
> e75 d_fe78 d_fe81 d_fe82 d_fe83 d_fe87 d_fe89 d_fe91 d_fe93 d_fe95 d_fe97 d_fe98 d_fe100 d_fe101 d_fe106 d_fe110 d_fe112 d_fe114 d_fe
> 118 d_fe121 d_fe132 d_fe133 d_fe135
Number of observations excluded: 855

note: year_fe13 omitted because of collinearity.

note: starting ppml estimation

Iteration 1: Deviance = 3.59e+09
Iteration 2: Deviance = 1.29e+09
Iteration 3: Deviance = 7.16e+08
Iteration 4: Deviance = 5.28e+08
Iteration 5: Deviance = 4.67e+08
Iteration 6: Deviance = 4.47e+08
Iteration 7: Deviance = 4.41e+08
Iteration 8: Deviance = 4.39e+08
Iteration 9: Deviance = 4.39e+08
Iteration 10: Deviance = 4.39e+08
Iteration 11: Deviance = 4.39e+08
Iteration 12: Deviance = 4.39e+08
Iteration 13: Deviance = 4.39e+08
Iteration 14: Deviance = 4.39e+08
Iteration 15: Deviance = 4.39e+08
Iteration 16: Deviance = 4.39e+08
Iteration 17: Deviance = 4.39e+08
Iteration 18: Deviance = 4.39e+08
Iteration 19: Deviance = 4.39e+08
Iteration 20: Deviance = 4.39e+08
Iteration 21: Deviance = 4.39e+08
Iteration 22: Deviance = 4.39e+08
Iteration 23: Deviance = 4.39e+08
Iteration 24: Deviance = 4.39e+08
Iteration 25: Deviance = 4.39e+08
Iteration 26: Deviance = 4.39e+08
Iteration 27: Deviance = 4.39e+08
Iteration 28: Deviance = 4.39e+08
Iteration 29: Deviance = 4.39e+08

Number of parameters: 98
Number of observations: 1185
Pseudo log-likelihood: -2.194e+08
R-squared: .96797438
Option strict is: off
------------------------------------------------------------------------------
| Robust
value0301 | Coefficient std. err. z P>|z| [95% conf. interval]
-------------+----------------------------------------------------------------
d_fe2 | -35.57109 . . . . .
d_fe3 | -43.71736 . . . . .
d_fe4 | -32.68027 . . . . .
d_fe5 | -49.18769 . . . . .
d_fe6 | -31.33228 . . . . .
d_fe7 | -35.30997 . . . . .
d_fe9 | -31.27861 . . . . .
d_fe10 | -26.25237 . . . . .
d_fe13 | -33.48132 . . . . .
......