Hi everyone,

I am trying to estimate marginal effects after having run a triple interaction (in a linear probability model) with one of the main effects being caught by the fixed effect. Therefore the coefficient of one of the main effects is omitted. I assume that that is why I cannot estimate marginal effects after running this model (from my understanding this should lead to the constant being "unestimable", right?). Is there a way for me to work around this (other than just running the model without fixed effects)? It is really the slope I am interested in, more so than any intercepts - so perhaps there is a way of just inserting a/any value for the intercept?! Also, in case it helps, the main effect which drops out is for a binary variable (which makes split sample analyses an option, but I wanted to see whether there are other ways of dealing with this first and leave that as a last resort).

In Stata terms, I am trying to estimate the following two lines below. I have also added a data example at the bottom of this, in case it helps illustrate my point.

Code:
areg Y c.X1##c.X2##i.X3, a(FE)

margins, dydx(X1) at(X2 = (0 (0.5) 4.5) X3=(0 1)) vsquish
Thanks so much for the help. Any input would be highly appreciated.


Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input long FE float(X2 Y X1 X3)
10143018 1.0986123 1  1.231193 1
10143018         0 0  .7493059 1
10143009         0 1 1.3820202 1
10143009 1.0986123 0  1.275593 1
10142872   2.70805 1  1.307223 0
10142872  3.135494 0  .9065389 0
10142848  2.772589 0 1.2386667 1
10142848         0 1 1.2820165 1
10142827  .6931472 0 1.2553422 1
10142827         0 1  1.379391 1
10142595  2.944439 1 1.0276814 1
10142595 4.3438053 0 1.0960358 1
10142594         0 0  .3706292 0
10142594 1.7917595 1 1.3194864 0
10142451   2.70805 1 1.0763656 1
10142451         0 0  .9885153 1
10142448   1.94591 1 1.2935138 1
10142448         0 0  1.277683 1
10142194  .6931472 1  1.271437 0
10142194  .6931472 0 1.0061655 0
10142182  .6931472 1 1.1844742 0
10142182   1.94591 0 1.2094254 0
10142124         0 1  .9228793 0
10142124  .6931472 0  .9958407 0
10142121  2.890372 0 1.0443183 1
10142121 3.4011974 1 1.1112077 1
10142115  2.397895 1 1.2370248 0
10142115   1.94591 0 1.1493943 0
10142076 1.0986123 0 1.2654196 1
10142076         0 1 1.3615023 1
10142013         0 0  .6853231 1
10142013         0 1 .27892447 1
10141965 2.0794415 0 1.1266971 1
10141965  .6931472 1 1.3776045 1
10141736 1.3862944 0  1.206344 1
10141736         0 1  .7457477 1
10141708         0 1  .6492612 0
10141708  .6931472 0 .57538915 0
10141638 4.1271343 1 1.3236197 1
10141638 2.3025851 0 1.3121105 1
10141615         0 0  .6377352 1
10141615  .6931472 1  1.289306 1
10141577 1.7917595 1  1.255946 1
10141577         0 0  .6630335 1
10141562  2.995732 1  .9476664 0
10141562   1.94591 0  .9631982 0
10141494         0 1  .7453708 0
10141494         0 0  .4729676 0
10141457 2.1972246 1  .7859399 1
10141457  2.397895 0  .8258712 1
10141314 1.3862944 1  1.349841 0
10141314 1.3862944 0 .55477506 0
10141191 1.7917595 1 1.0532235 0
10141191 1.0986123 0   .951867 0
10141120 1.7917595 1  .7505929 1
10141120  2.995732 0 1.2242912 1
10141086  .6931472 0 1.1190642 0
10141086         0 1 1.0493766 0
10141084 2.0794415 1  .9811458 1
10141084   1.94591 0  .9422299 1
10140945         0 1  .8014972 0
10140945         0 0  .6099473 0
10140814         0 0 .26569033 0
10140814 1.7917595 1 1.3924794 0
10140812  .6931472 0 .34921825 0
10140812         0 1 1.4015034 0
10140800   2.70805 1  .5356797 0
10140800 3.9512436 0  .9135412 0
10140781         0 0 1.0477619 1
10140781 1.0986123 1 1.0129087 1
10140746 4.6051702 1 1.1419833 1
10140746  4.844187 0 1.1674879 1
10140699         0 1  .9487832 1
10140699  .6931472 0  1.068078 1
10140628 2.1972246 0 1.0852195 0
10140628  2.564949 1 1.0986866 0
10140598 3.5263605 1 1.1449307 1
10140598         0 0  .6565773 1
10140532         0 0 1.1055694 1
10140532 3.0445225 1 1.1195079 1
10140387  3.295837 1  1.158425 0
10140387  3.871201 0 1.1757511 0
10140376  3.135494 1  1.209297 1
10140376 1.3862944 0 1.0646921 1
10140367         0 0  .9131414 0
10140367 1.3862944 1 1.1242832 0
10140360         0 1 1.3866943 0
10140360         0 0  .4267809 0
10140339         0 0 .59409094 0
10140339         0 1  .6886784 0
10140333 4.0775375 0  .7903495 0
10140333   5.32301 1 1.2403438 0
10140308 1.0986123 0  .6452845 0
10140308         0 1 1.3927143 0
10140302         0 1 1.2086025 1
10140302  .6931472 0  1.273538 1
10140301  .6931472 1  .9430985 0
10140301 2.1972246 0 1.0170584 0
10140294 1.0986123 1 1.2636527 1
10140294   1.94591 0 1.2083483 1
10140283  3.218876 0 1.1499057 1
10140283   3.89182 1 1.1415253 1
10140278   3.89182 0 1.1802843 0
10140278  3.988984 1 1.1738459 0
10140264 1.0986123 0 1.0200553 0
10140264 3.3322046 1 1.1923115 0
10140208         0 1  .4746172 0
10140208         0 0  .5337738 0
10140177  2.484907 1  .7916193 0
10140177 1.0986123 0  .7291901 0
10140166         . 1         . 1
10140166         0 0  .9897379 1
10140106  1.609438 0 1.2267642 1
10140106  2.772589 1 1.1130809 1
10140011  1.609438 0 1.1362092 0
10140011 1.3862944 1  .9261208 0
10140009  .6931472 0  .8951876 1
10140009         0 1 1.2029034 1
10140000  .6931472 0 1.2000805 0
10140000         0 1 1.1441292 0
10139988         0 1  .9697916 0
10139988         0 0  .8390914 0
10139858         0 0  .6079006 0
10139858 2.0794415 1 1.2491387 0
10139843         0 0 .54556745 0
10139843         0 1  .6603725 0
10139812   1.94591 0  .9233347 1
10139812  1.609438 1 1.0238228 1
10139787 1.0986123 0  .9764532 0
10139787         0 1  .9589562 0
10139361 2.1972246 1    .90837 1
10139361 2.6390574 0  .9134418 1
10139270 2.6390574 1   .820991 0
10139270  .6931472 0  .4257423 0
10139169         0 0 .53702605 1
10139169  .6931472 1 .57743293 1
10139115  1.609438 1  .8493823 0
10139115   1.94591 0  .8016933 0
10138951         0 0  .5689031 1
10138951  3.433987 1  .7854894 1
10138812 4.3307333 1 1.0107682 1
10138812  1.609438 0  .8471205 1
10138759 1.3862944 1 .52097213 1
10138759  2.484907 0 1.2818906 1
10138697         0 0  .9308263 0
10138697  2.397895 1  .9227511 0
10138356         0 0  .7657002 1
10138356 2.6390574 1 1.1339812 1
10138339  4.553877 0  .9919426 1
10138339  4.394449 1  .9746974 1
10138323  5.214936 1 1.0302064 1
10138323 3.4011974 0  .9377924 1
10138293 3.2580965 1 1.1161833 0
10138293 3.0445225 0 1.0981154 0
10138169 2.0794415 1 1.0170199 1
10138169         0 0  .8199392 1
10138120   2.70805 1 1.1370072 1
10138120 1.0986123 0 1.1125196 1
10138106 1.7917595 0  .6866983 0
10138106   1.94591 1  .7396418 0
10137864   1.94591 1  .7463221 1
10137864  1.609438 0  .7103068 1
10137812 2.0794415 1  .5715956 0
10137812         0 0  .5133577 0
10137739  5.147494 0  .3986844 1
10137739 3.6888795 1  .3789936 1
10137651         0 1  .8744928 1
10137651  2.397895 0  .8746155 1
10137625 1.3862944 0  .7511013 1
10137625   3.89182 1 1.0652461 1
10137546         0 1  1.080466 1
10137546  .6931472 0  .7146025 1
10137523  1.609438 0  .8756774 0
10137523  .6931472 1  .8779976 0
10137472 2.0794415 0 1.1346102 0
10137472         0 1  .9702966 0
10137304 1.0986123 1 1.3779285 0
10137304   2.70805 0  .3735727 0
10137271 3.9318256 1 1.1716378 1
10137271 1.3862944 0 .38255095 1
10137269 3.4011974 1 1.0100572 1
10137269  3.433987 0  .9836367 1
10137265 3.9318256 0 1.3762392 0
10137265  3.135494 1 1.2902777 0
10137260  2.995732 1  .7448317 1
10137260  2.833213 0  .7563806 1
10137229  3.218876 0 1.1252269 0
10137229 3.8501475 1  .5704803 0
10137208         0 1 1.1243953 1
10137208 1.0986123 0  1.187624 1
10137180  2.484907 0  .3911843 1
10137180   2.70805 1  .3868511 1
10137135         0 0  .3389531 1
10137135         . 1         . 1
10136909 2.1972246 1 1.3949153 1
10136909         0 0  .4510143 1
10136862         0 1 1.0193574 1
10136862  1.609438 0  .9723536 1
10136850  4.976734 1  .6832581 1
10136850 3.9318256 0  .6890621 1
end
label values Y realized
label def realized 0 "Not Observed", modify
label def realized 1 "Observed Patent-Lawyer Pair", modify