Hi Statalist!

First of all, thank you for all the help you give on a daily basis! It's been very helpful to lurk here, but now I find myself in a situation where I simply need to ask.

I'm trying to regress Return (depvar) on RMRF SMB HML LIQ (indepvars) using panel data. My panel data set is companynum and date (monthly data 2007-03 to 2016-12), however, it is unbalanced.

Now, when I use the different xtreg options (fe, re, et cetera) and xtgls, I get different results than when I use statsby or regressby. Furthermore, my regression results are quite different than expected, but less so when using statsby or regressby.

I'm now asking: 1) which method to use (xt or statsby), and 2) if I'm using them incorrectly?
I'm posting a smaller sample of my data and my regression commands. Not sure if the dataex output has been formatting correctly for your use, but the columns are date, companynum, RMRF, SMB, HML, LIQ. Date was reformatted in dataex, but is normally in a YYYYmm format (e.g. 2007m3).

The different commands I've been using:
. xtreg Return RMRF SMB HML LIQ, re vce(robust)
statsby _b, by(companynum): reg Return RMRF SMB HML LIQ, robust noconstant

The means of my coefficients from statsby are larger than the coefficients from xtreg. Shouldn't the mean of the coefficients from statsby be equal to the coefficients in the other reg commands, or is it wrong to think of it that way?

Please let me know if you find anything that is incorrectly posted and I will correct accordingly.
Many thanks!

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input float(date companynum) double(RMRF SMB HML LIQ)
677 1  -.03567645   .031857625   .035097148 -.0038981533515944945
678 1   .05532546  .0022137638  .0075254366  .0028282892888514353
679 1  .024670409  .0083233304  .0063983239   .048738782080504486
680 1  .017835448   .025180005  -.006078342    .02920958419799065
681 1 -.009465334  .0084097693   .012648277  -.011058678221505731
682 1  .014410426  .0064111673  -.012598168   -.01494227358556188
683 1  .029204069  -.026093747   .018178428  -.014232915409939827
566 2  .052961133   .021395301 -.0039642137  -.036748350946197265
567 2  .064811036   -.02813188  -.015092619  -.017005912917779073
568 2  .016662389  -.046779487   .004310017   .003553448955587013
569 2 -.024995016   .011651467  -.011661232  -.023518709608667893
570 2 -.017945765  .0046412922    .03236885  .0056249091016424176
571 2 -.029406879 -.0016394301 -.0072570862   .025491101902319215
end
format %tm date