我的模型结果如下，请教一个回归-经管之家官网！

这是我的动态因子模型（时间序列）算出来的结果，您帮我看看吧，我昨晚一夜都没睡
. dfactor ( x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 x12 x13 x14 x15 = , noconstant) (f1 f2 = , ar(1/2))
note: attempting to estimate 49 parameters using 71 observations
searching for initial values
(setting technique to bhhh)
Iteration 0: log likelihood =1215,8381(not concave)
Iteration 1: log likelihood =1264,2708(not concave)
Iteration 2: log likelihood =1349,6326
Iteration 3: log likelihood =1353,4891(backed up)
Iteration 4: log likelihood =1353,5922(backed up)
(switching technique to nr)
Iteration 5: log likelihood =1353,7206(not concave)
Iteration 6: log likelihood = 1376,132(not concave)
Iteration 7: log likelihood =1409,7632(not concave)
Iteration 8: log likelihood =1549,2646(not concave)
Iteration 9: log likelihood =1638,7129(not concave)
Iteration 10:log likelihood =1656,5544
Iteration 11:log likelihood =1660,4573(not concave)
Iteration 12:log likelihood =1667,9202(not concave)
Iteration 13:log likelihood =1668,3741(not concave)
Iteration 14:log likelihood =1668,8772(not concave)
Iteration 15:log likelihood =1669,1098
Iteration 16:log likelihood =1670,9668
Iteration 17:log likelihood =1671,5493
Iteration 18:log likelihood =1671,5852
Iteration 19:log likelihood =1671,5857
Iteration 20:log likelihood =1671,5857
Refining estimates:
Iteration 0: log likelihood =1671,5857
Iteration 1: log likelihood =1671,5857
Dynamic-factor model
Sample: 1993q2 - 2010q4 Number of obs = 71
Wald chi2(34) = 4141,00
Log likelihood =1671,5857 Prob > chi2 = 0,0000
------------------------------------------------------------------------------
| OIM
| Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
f1 |
f1 |
L1. | ,8665387 ,3695545 2,34 0,019 ,1422252 1,590852
L2. | ,117913 ,367528 0,32 0,748 -,6024286 ,8382546
-------------+----------------------------------------------------------------
f2 |
f2 |
L1. | ,3149916 ,1342648 2,35 0,019 ,0518373 ,5781459
L2. | ,1111472 ,1313133 0,85 0,397 -,1462222 ,3685166
-------------+----------------------------------------------------------------
x1 |
f1 |-,0062286 ,0022052 -2,82 0,005 -,0105507 -,0019065
f2 | ,0078439 ,004808 1,63 0,103 -,0015796 ,0172674
-------------+----------------------------------------------------------------
x2 |
f1 | -,007689 ,00249 -3,09 0,002 -,0125693 -,0028088
f2 | ,0097354 ,0033133 2,94 0,003 ,0032415 ,0162294
-------------+----------------------------------------------------------------
x3 |
f1 |-,0088181 ,0033543 -2,63 0,009 -,0153925 -,0022438
f2 | ,0121158 ,0090162 1,34 0,179 -,0055556 ,0297872
-------------+----------------------------------------------------------------
x4 |
f1 |-,0077154 ,0024953 -3,09 0,002 -,0126061 -,0028246
f2 | ,0027643 ,0022931 1,21 0,228 -,0017302 ,0072588
-------------+----------------------------------------------------------------
x5 |
f1 |-,0088336 ,0030365 -2,91 0,004 -,0147851 -,002882
f2 | ,0205638 ,0062767 3,28 0,001 ,0082616 ,032866
-------------+----------------------------------------------------------------
x6 |
f1 |-,0077471 ,0029429 -2,63 0,008 -,0135151 -,0019791
f2 | ,0253554 ,0076018 3,34 0,001 ,0104561 ,0402546
-------------+----------------------------------------------------------------
x7 |
f1 |-,0088841 ,0029114 -3,05 0,002 -,0145903 -,0031778
f2 | ,0116644 ,003491 3,34 0,001 ,0048222 ,0185067
-------------+----------------------------------------------------------------
x8 |
f1 |-,0044779 ,0017396 -2,57 0,010 -,0078874 -,0010684
f2 |-,0071166 ,0047901 -1,49 0,137 -,0165051 ,0022719
-------------+----------------------------------------------------------------
x9 |
f1 |-,0045033 ,0015092 -2,98 0,003 -,0074613 -,0015452
f2 | ,0032146 ,0027992 1,15 0,251 -,0022717 ,008701
-------------+----------------------------------------------------------------
x10 |
f1 |-,0146674 ,0047045 -3,12 0,002 -,0238881 -,0054466
f2 | ,0049748 ,0049444 1,01 0,314 -,0047161 ,0146657
-------------+----------------------------------------------------------------
x11 |
f1 | ,0016096 ,0060718 0,27 0,791 -,0102909 ,0135101
f2 | ,1424213 ,0157182 9,06 0,000 ,1116142 ,1732285
-------------+----------------------------------------------------------------
x12 |
f1 | ,0025023 ,0055643 0,45 0,653 -,0084036 ,0134081
f2 | ,1331423 ,0134419 9,90 0,000 ,1067966 ,1594881
-------------+----------------------------------------------------------------
x13 |
f1 | ,0021386 ,0054283 0,39 0,694 -,0085006 ,0127779
f2 | ,1260046 ,0141463 8,91 0,000 ,0982784 ,1537308
-------------+----------------------------------------------------------------
x14 |
f1 | ,0027549 ,0043746 0,63 0,529 -,0058192 ,011329
f2 | ,0955616 ,0125616 7,61 0,000 ,0709413 ,1201819
-------------+----------------------------------------------------------------
x15 |
f1 | ,0000163 ,0012787 0,01 0,990 -,0024899 ,0025225
f2 | ,0190928 ,0053239 3,59 0,000 ,0086582 ,0295274
-------------+----------------------------------------------------------------
var(e.x1) | ,0016575 ,0002825 5,87 0,000 ,0011039 ,0022112
var(e.x2) | ,0006807 ,0001202 5,66 0,000 ,000445 ,0009164
var(e.x3) | ,0060194 ,0010193 5,91 0,000 ,0040216 ,0080172
var(e.x4) | ,0002809 ,000057 4,92 0,000 ,0001691 ,0003927
var(e.x5) | ,0026263 ,0004581 5,73 0,000 ,0017285 ,003524
var(e.x6) | ,0038985 ,0006717 5,80 0,000 ,002582 ,005215
var(e.x7) | ,0007078 ,0001324 5,34 0,000 ,0004482 ,0009674
var(e.x8) | ,0016904 ,0002862 5,91 0,000 ,0011295 ,0022512
var(e.x9) | ,0005557 ,0000954 5,83 0,000 ,0003688 ,0007426
var(e.x10) | ,0014524 ,0002875 5,05 0,000 ,0008889 ,0020159
var(e.x11) | ,0071789 ,0015531 4,62 0,000 ,0041349 ,0102229
var(e.x12) | ,0030264 ,0009585 3,16 0,002 ,0011477 ,0049052
var(e.x13) | ,0063473 ,0013666 4,64 0,000 ,0036688 ,0090258
var(e.x14) | ,006931 ,0012921 5,36 0,000 ,0043986 ,0094634
var(e.x15) | ,0019808 ,0003381 5,86 0,000 ,0013181 ,0026435
------------------------------------------------------------------------------
Note: Tests of variances against zero are conservative and are provided only for reference.
我想要把这里面的f1 和 f2 提取出来，那个模型里面好像没有把f1 f2 看成是变量，您帮帮我好吗？感激不尽啊
怎样回归出f1 和 f2?