I am wondering if it makes sense to add my additional three variables as sensitivity check, as I normally read papers that only change one variables or so when doing sensitivity check.
If yes, then how do I interpret the outcome of the new addition? Does that mean my model is robust? Thank you
Code:
. reg mtd prof size tang growth liq dc1 dc2 dc3 dc4 i.industry i.year, vce(robust)
Linear regression Number of obs = 4,820
F(27, 4792) = 80.96
Prob > F = 0.0000
R-squared = 0.3569
Root MSE = .18635
------------------------------------------------------------------------------
| Robust
mtd | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
prof | -.3832766 .0686802 -5.58 0.000 -.5179214 -.2486318
size | .0361722 .002791 12.96 0.000 .0307006 .0416438
tang | .1248675 .0183476 6.81 0.000 .0888977 .1608372
growth | -.0083867 .002782 -3.01 0.003 -.0138407 -.0029326
liq | -.0102962 .0036118 -2.85 0.004 -.017377 -.0032155
dc1 | .0615475 .0120496 5.11 0.000 .0379248 .0851702
dc2 | .0420181 .0074354 5.65 0.000 .0274413 .0565949
dc3 | .0213742 .008977 2.38 0.017 .0037751 .0389732
dc4 | .0781161 .0124258 6.29 0.000 .0537559 .1024763
|
industry |
9991 | -.0240652 .0212285 -1.13 0.257 -.0656827 .0175523
9992 | .044358 .0242483 1.83 0.067 -.0031799 .0918958
9993 | -.189325 .0237744 -7.96 0.000 -.2359337 -.1427163
9994 | .0256848 .0208052 1.23 0.217 -.015103 .0664726
9995 | -.1267229 .0254384 -4.98 0.000 -.1765938 -.076852
9996 | .069327 .0208692 3.32 0.001 .0284139 .1102402
9997 | -.0659017 .0222793 -2.96 0.003 -.1095794 -.022224
9998 | -.0668792 .0243165 -2.75 0.006 -.1145507 -.0192076
9999 | -.0184298 .0227901 -0.81 0.419 -.0631089 .0262493
|
year |
2009 | -.0585273 .0127497 -4.59 0.000 -.0835225 -.0335321
2010 | -.0834637 .0124864 -6.68 0.000 -.1079427 -.0589847
2011 | -.0741264 .0125177 -5.92 0.000 -.0986667 -.049586
2012 | -.0744085 .012312 -6.04 0.000 -.0985456 -.0502714
2013 | -.1149802 .0122686 -9.37 0.000 -.1390323 -.0909281
2014 | -.1233646 .0124957 -9.87 0.000 -.1478619 -.0988673
2015 | -.1275209 .0127533 -10.00 0.000 -.1525233 -.1025186
2016 | -.1244362 .0129507 -9.61 0.000 -.1498255 -.0990469
2017 | -.132976 .012769 -10.41 0.000 -.1580091 -.1079429
|
_cons | .0279881 .0510031 0.55 0.583 -.0720014 .1279777
------------------------------------------------------------------------------
Code:
. reg mtd prof size tang growth liq dividendpayout ntds intcoverage dc1 dc2 dc3 dc4 i.industry i.year, vce(robust)
Linear regression Number of obs = 4,820
F(30, 4789) = 75.58
Prob > F = 0.0000
R-squared = 0.3601
Root MSE = .18593
--------------------------------------------------------------------------------
| Robust
mtd | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------+----------------------------------------------------------------
prof | -.3903113 .0725805 -5.38 0.000 -.5326024 -.2480202
size | .0354715 .0028602 12.40 0.000 .0298643 .0410787
tang | .1397064 .0171498 8.15 0.000 .1060849 .173328
growth | -.0078057 .0028427 -2.75 0.006 -.0133787 -.0022327
liq | -.0104448 .0036926 -2.83 0.005 -.017684 -.0032056
dividendpayout | -.0018741 .0010861 -1.73 0.085 -.0040034 .0002552
ntds | -.3816906 .1609167 -2.37 0.018 -.6971612 -.06622
intcoverage | -1.42e-06 1.22e-06 -1.16 0.246 -3.81e-06 9.78e-07
dc1 | .0564218 .0129878 4.34 0.000 .0309596 .0818839
dc2 | .0397018 .0076943 5.16 0.000 .0246175 .0547861
dc3 | .019007 .0089529 2.12 0.034 .0014553 .0365588
dc4 | .074056 .012708 5.83 0.000 .0491424 .0989696
|
industry |
9991 | -.0253018 .0210576 -1.20 0.230 -.0665843 .0159807
9992 | .0452068 .0240588 1.88 0.060 -.0019594 .092373
9993 | -.1899307 .0236659 -8.03 0.000 -.2363267 -.1435348
9994 | .0250902 .0206563 1.21 0.225 -.0154057 .0655861
9995 | -.1287157 .0254462 -5.06 0.000 -.1786019 -.0788295
9996 | .0628662 .020813 3.02 0.003 .0220633 .1036692
9997 | -.0576092 .022123 -2.60 0.009 -.1009805 -.0142379
9998 | -.0559507 .024707 -2.26 0.024 -.1043879 -.0075136
9999 | -.0166227 .0226275 -0.73 0.463 -.0609829 .0277375
|
year |
2009 | -.0591305 .0127057 -4.65 0.000 -.0840396 -.0342214
2010 | -.0841003 .0124556 -6.75 0.000 -.108519 -.0596816
2011 | -.0743277 .012466 -5.96 0.000 -.0987668 -.0498887
2012 | -.0741434 .012242 -6.06 0.000 -.0981434 -.0501435
2013 | -.1163413 .0121812 -9.55 0.000 -.1402221 -.0924605
2014 | -.1243697 .0123403 -10.08 0.000 -.1485625 -.100177
2015 | -.1280807 .0126523 -10.12 0.000 -.152885 -.1032764
2016 | -.1253472 .0128311 -9.77 0.000 -.1505021 -.1001923
2017 | -.1342228 .0126918 -10.58 0.000 -.1591047 -.109341
|
_cons | .0459086 .0528146 0.87 0.385 -.0576323 .1494496
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