I am running an xtreg-fe cluster(panelvar) on monthly data. I employ time-fixed effects too. However, the regression output contains too many predictors. The number of predictors> the number of clusters. Is there a way out of this predicament?
Here is my code:
Code:
xtreg laod lnl lpr lt lh lp lwi lf la lpop lndvi i.month, fe cluster(sd)
Code:
note: lpop omitted because of collinearity
Fixed-effects (within) regression Number of obs = 5,826
Group variable: sd Number of groups = 59
R-sq: Obs per group:
within = 0.3639 min = 80
between = 0.5746 avg = 98.7
overall = 0.4326 max = 102
F(58,58) = .
corr(u_i, Xb) = -0.3306 Prob > F = .
(Std. Err. adjusted for 59 clusters in sd)
------------------------------------------------------------------------------
| Robust
laod | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lnl | .0444047 .0189316 2.35 0.022 .006509 .0823003
lpr | .0073755 .0040048 1.84 0.071 -.000641 .0153921
lt | -3.884795 1.871788 -2.08 0.042 -7.631587 -.1380035
lh | .5299972 .0476566 11.12 0.000 .4346021 .6253922
lp | 4.666758 6.377097 0.73 0.467 -8.098389 17.4319
lwi | -.3078047 .058291 -5.28 0.000 -.4244869 -.1911225
lf | .565516 .6729575 0.84 0.404 -.7815548 1.912587
la | .307257 .1024781 3.00 0.004 .1021247 .5123892
lpop | 0 (omitted)
lndvi | -.2585523 .0444562 -5.82 0.000 -.347541 -.1695636
|
month |
649 | -.1233491 .0306944 -4.02 0.000 -.1847905 -.0619077
650 | -.0332226 .0468975 -0.71 0.482 -.127098 .0606529
651 | .1017779 .0643089 1.58 0.119 -.0269503 .230506
652 | .0465545 .0721204 0.65 0.521 -.0978102 .1909193
653 | .1329436 .1007038 1.32 0.192 -.0686368 .3345241
654 | .0536357 .1015976 0.53 0.600 -.149734 .2570054
655 | -.1270271 .0812046 -1.56 0.123 -.2895757 .0355216
656 | -.3396463 .0666669 -5.09 0.000 -.4730947 -.206198
657 | -.1076306 .0529593 -2.03 0.047 -.2136402 -.001621
658 | .0830799 .0496674 1.67 0.100 -.0163402 .1825
659 | -.121576 .0278431 -4.37 0.000 -.17731 -.065842
660 | -.1345138 .0250915 -5.36 0.000 -.1847399 -.0842877
661 | -.1078254 .0322146 -3.35 0.001 -.17231 -.0433409
662 | -.0871166 .0491051 -1.77 0.081 -.1854111 .0111779
663 | .0174388 .0497147 0.35 0.727 -.0820761 .1169536
664 | .2145259 .0837729 2.56 0.013 .0468362 .3822157
665 | .031304 .1028956 0.30 0.762 -.1746639 .2372719
666 | .2418876 .1020404 2.37 0.021 .0376316 .4461436
667 | -.0446145 .082237 -0.54 0.590 -.2092298 .1200008
668 | -.2410526 .0697299 -3.46 0.001 -.3806322 -.101473
669 | -.091523 .0435524 -2.10 0.040 -.1787026 -.0043434
670 | -.0992061 .056894 -1.74 0.087 -.2130917 .0146795
671 | -.2472565 .0264505 -9.35 0.000 -.3002029 -.1943101
672 | -.0079172 .0290274 -0.27 0.786 -.0660218 .0501874
673 | .0613458 .0326798 1.88 0.066 -.00407 .1267615
674 | .0185414 .0510152 0.36 0.718 -.0835766 .1206594
675 | .0246948 .0633253 0.39 0.698 -.1020646 .1514542
676 | -.0176356 .084514 -0.21 0.835 -.1868087 .1515375
677 | -.1145517 .1096654 -1.04 0.301 -.334071 .1049675
678 | -.0882696 .1083409 -0.81 0.419 -.3051375 .1285984
679 | -.1288769 .0862318 -1.49 0.140 -.3014886 .0437348
680 | -.252661 .0796029 -3.17 0.002 -.4120035 -.0933185
681 | -.1868149 .0611212 -3.06 0.003 -.3091622 -.0644676
682 | .0267119 .0466807 0.57 0.569 -.0667296 .1201534
683 | -.1697311 .0430275 -3.94 0.000 -.2558601 -.0836022
684 | -.0028064 .0208123 -0.13 0.893 -.0444668 .038854
685 | -.0862943 .0407558 -2.12 0.039 -.1678759 -.0047127
686 | -.005816 .0440132 -0.13 0.895 -.0939181 .0822861
687 | .2303101 .066068 3.49 0.001 .0980606 .3625596
688 | .0515448 .0816173 0.63 0.530 -.1118301 .2149196
689 | -.1343553 .1008913 -1.33 0.188 -.3363112 .0676005
690 | -.071389 .1032045 -0.69 0.492 -.2779752 .1351972
691 | -.0436445 .101499 -0.43 0.669 -.2468168 .1595278
692 | -.3623209 .0795405 -4.56 0.000 -.5215385 -.2031033
693 | -.137231 .0668359 -2.05 0.045 -.2710177 -.0034443
694 | .0502787 .0588681 0.85 0.397 -.0675586 .168116
695 | -.0297487 .0249504 -1.19 0.238 -.0796923 .0201949
696 | .2128178 .0492536 4.32 0.000 .114226 .3114095
697 | -.1318354 .0319908 -4.12 0.000 -.1958719 -.0677989
698 | .1500738 .0592733 2.53 0.014 .0314254 .2687222
699 | .0708001 .0651461 1.09 0.282 -.0596041 .2012042
700 | .1466561 .0819551 1.79 0.079 -.0173948 .3107071
701 | .0960467 .1118363 0.86 0.394 -.127818 .3199115
702 | -.0199497 .1224544 -0.16 0.871 -.2650688 .2251694
703 | .1611545 .1091591 1.48 0.145 -.0573511 .3796602
704 | -.3932091 .0616178 -6.38 0.000 -.5165505 -.2698677
705 | -.1384157 .0415515 -3.33 0.002 -.2215901 -.0552413
706 | -.1232199 .0393126 -3.13 0.003 -.2019126 -.0445272
707 | .0229704 .0443862 0.52 0.607 -.0658783 .1118191
708 | .003345 .0300747 0.11 0.912 -.056856 .0635461
709 | -.1586821 .0255624 -6.21 0.000 -.2098508 -.1075134
710 | -.0659489 .0494828 -1.33 0.188 -.1649995 .0331018
711 | -.0910197 .0650305 -1.40 0.167 -.2211924 .039153
712 | .1705902 .0803942 2.12 0.038 .0096637 .3315167
713 | -.0349302 .0975616 -0.36 0.722 -.230221 .1603607
714 | .0457064 .1174711 0.39 0.699 -.1894375 .2808503
715 | -.091042 .0997719 -0.91 0.365 -.2907573 .1086732
716 | -.2498159 .0979934 -2.55 0.013 -.4459709 -.0536608
717 | -.3566553 .057478 -6.21 0.000 -.4717101 -.2416006
718 | .0368881 .0480439 0.77 0.446 -.0592823 .1330585
719 | -.099185 .0316461 -3.13 0.003 -.1625316 -.0358384
720 | -.1638996 .0166212 -9.86 0.000 -.1971706 -.1306286
721 | -.0645378 .0305725 -2.11 0.039 -.1257354 -.0033402
722 | -.1241648 .043424 -2.86 0.006 -.2110874 -.0372422
723 | -.0614872 .0544523 -1.13 0.263 -.1704854 .047511
724 | -.0086622 .0832141 -0.10 0.917 -.1752334 .157909
725 | -.213926 .1238311 -1.73 0.089 -.4618009 .0339488
726 | -.2437386 .1041832 -2.34 0.023 -.4522839 -.0351932
727 | -.1545941 .1188872 -1.30 0.199 -.3925728 .0833845
728 | -.3049369 .0829763 -3.67 0.001 -.4710321 -.1388417
729 | -.0042765 .0881209 -0.05 0.961 -.1806697 .1721166
730 | .0111962 .0514665 0.22 0.829 -.0918253 .1142177
731 | -.0253891 .0363513 -0.70 0.488 -.0981542 .0473761
732 | .1024035 .0263547 3.89 0.000 .0496489 .1551581
733 | .0508763 .0299352 1.70 0.095 -.0090456 .1107982
734 | .2174193 .0536344 4.05 0.000 .1100584 .3247801
735 | .2987526 .0671605 4.45 0.000 .1643162 .4331891
736 | -.0379749 .0809879 -0.47 0.641 -.2000899 .1241401
737 | -.0257866 .1015697 -0.25 0.800 -.2291004 .1775271
738 | -.0003278 .1141334 -0.00 0.998 -.2287907 .2281351
739 | -.0071986 .0921522 -0.08 0.938 -.1916612 .1772641
740 | -.2660276 .0953891 -2.79 0.007 -.4569696 -.0750856
741 | -.1511299 .0671898 -2.25 0.028 -.2856248 -.016635
742 | -.0220244 .0602739 -0.37 0.716 -.1426757 .0986269
743 | .1625696 .0367626 4.42 0.000 .0889812 .236158
744 | -.0285415 .0379808 -0.75 0.455 -.1045682 .0474853
745 | .0387975 .044154 0.88 0.383 -.0495863 .1271812
746 | .322907 .0602675 5.36 0.000 .2022685 .4435455
747 | .3102825 .0777607 3.99 0.000 .1546276 .4659374
748 | .3304651 .0885366 3.73 0.000 .1532399 .5076904
749 | .2293189 .1110574 2.06 0.043 .0070134 .4516244
|
_cons | -21.81826 76.01927 -0.29 0.775 -173.9874 130.3508
-------------+----------------------------------------------------------------
sigma_u | .16010163
sigma_e | .22657205
rho | .33303091 (fraction of variance due to u_i)
------------------------------------------------------------------------------
0 Response to Too many predictors when using Time fixed effects for panel data
Post a Comment