Hoping someone can help me understand an issue with using test after margins, post.
I run the following regression, with the ultimate aim of knowing whether the effect of OM varies meaningfully with the value of IPcont.
Following the directions in the entry for margins, I run this margins command including the post option.
I've checked that the values for dy/dx are what manual calculation of 0.15 + IPcont * _b[c.IPcont#c.OM] gives. Based on the strongly overlapping 95% CI, I wouldn't expect the difference between IPcont = 0 and IPcont = 17.5 to be significant. However, the difference between IPcont = 0 and IPcont = 82.5 might be.
I use margins, coefl to get the names of the coefficient and then do the tests corresponding to the above.
Two very odd things result. First, I don't believe that [OM]1bn._at and [OM]2._at = 0 differ at the 0.0218 level. Second, I get exactly the same values when testing for the difference [OM]1bn._at - [OM]5._at = 0. That should NOT be the case if I've tested what I think I've tested.
So, I'm baffled and concerned that I'm not testing what I intend. Can anyone help me properly test whether the marginal effect of OM differs when IPcont=0 versus when IPcont=82.5?
Thank you.
I run the following regression, with the ultimate aim of knowing whether the effect of OM varies meaningfully with the value of IPcont.
Code:
. regress Opp $controls ib0.csDum c.IPcont OM BM Sol c.IPcont#c.OM, robust
--------------------------------------------------------------------------------------------
| Robust
Opp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
---------------------------+----------------------------------------------------------------
depAsym | -.0121849 .0275436 -0.44 0.659 -.0667485 .0423786
volUnc | .0653512 .0494453 1.32 0.189 -.0325996 .163302
tecUnc | -.1130619 .0964371 -1.17 0.243 -.3041031 .0779794
perfAmb | .2317594 .0863513 2.68 0.008 .0606981 .4028206
prior | .0173488 .0850643 0.20 0.839 -.151163 .1858605
localSupp | .163849 .1866408 0.88 0.382 -.205885 .5335829
noExtSupp | .0056782 .0084204 0.67 0.501 -.0110026 .0223591
emplMARKUSln | .1257304 .0781502 1.61 0.110 -.0290846 .2805454
|
INP |
Input 1 | -.0157771 .334995 -0.05 0.963 -.6793996 .6478455
Input 3 | -.1744805 .3404611 -0.51 0.609 -.8489313 .4999703
|
contractSpecMean | .0300773 .0590932 0.51 0.612 -.0869859 .1471404
|
csDum |
Concurrent sourcing dummy | .2245481 .2263385 0.99 0.323 -.2238266 .6729229
IPcont | .0237525 .0134547 1.77 0.080 -.0029012 .0504062
OM | .1582368 .0675551 2.34 0.021 .0244106 .2920629
BM | -.0370055 .0642838 -0.58 0.566 -.1643513 .0903403
Sol | -.4137848 .0793497 -5.21 0.000 -.570976 -.2565937
|
c.IPcont#c.OM | -.0047667 .0020494 -2.33 0.022 -.0088266 -.0007067
|
_cons | 1.929306 .9005282 2.14 0.034 .1453665 3.713246
--------------------------------------------------------------------------------------------
Code:
. margins, dydx(OM) at(IPcont=(0 17.5 37.5 62.5 82.5)) post
Expression : Linear prediction, predict()
dy/dx w.r.t. : OM
1._at : IPcont = 0
2._at : IPcont = 17.5
3._at : IPcont = 37.5
4._at : IPcont = 62.5
5._at : IPcont = 82.5
------------------------------------------------------------------------------
| Delta-method
| dy/dx Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
OM |
_at |
1 | .1582368 .0675551 2.34 0.021 .0244106 .2920629
2 | .0748203 .0623266 1.20 0.232 -.0486482 .1982887
3 | -.0205129 .0791117 -0.26 0.796 -.1772326 .1362069
4 | -.1396793 .1181164 -1.18 0.239 -.373667 .0943084
5 | -.2350124 .1545508 -1.52 0.131 -.5411763 .0711514
------------------------------------------------------------------------------
I use margins, coefl to get the names of the coefficient and then do the tests corresponding to the above.
Code:
. margins, coefl
Average marginal effects Number of obs = 132
Model VCE : Robust
Expression : Linear prediction, predict()
dy/dx w.r.t. : OM
1._at : IPcont = 0
2._at : IPcont = 17.5
3._at : IPcont = 37.5
4._at : IPcont = 62.5
5._at : IPcont = 82.5
------------------------------------------------------------------------------
| dy/dx Legend
-------------+----------------------------------------------------------------
OM |
_at |
1 | .1582368 _b[OM:1bn._at]
2 | .0748203 _b[OM:2._at]
3 | -.0205129 _b[OM:3._at]
4 | -.1396793 _b[OM:4._at]
5 | -.2350124 _b[OM:5._at]
------------------------------------------------------------------------------
. test _b[OM:1bn._at] = _b[OM:2._at]
( 1) [OM]1bn._at - [OM]2._at = 0
F( 1, 114) = 5.41
Prob > F = 0.0218
. test _b[OM:1bn._at] = _b[OM:5._at]
( 1) [OM]1bn._at - [OM]5._at = 0
F( 1, 114) = 5.41
Prob > F = 0.0218
So, I'm baffled and concerned that I'm not testing what I intend. Can anyone help me properly test whether the marginal effect of OM differs when IPcont=0 versus when IPcont=82.5?
Thank you.
