Hi Stata Experts:

I am using xtabond2 to estimate the impact of interaction term on outcome variable. The interaction term is based on two categorical variables.


My code is

Code:
xtabond2 lfstfy L.lfstfy i.A i.A#i.B i.housect i.Rstate i.year, gmmstyle(lfstfy i.A i.A#i.B, laglimits(2 3) collapse) ivstyle(i.Rstate i.year, equation(level)) noleveleq robust orthogonal
i.A#i.B is my main variable. I did not add B in the model, because the results omit B as well.

The results show some of cells of interaction terms were omitted. I assume it may be because collinearity. As interaction term is my interest variable, can someone tell me how to recover the omitted categories?

Code:
A
1 0 (empty)
2 -.253001 .3413615 -0.74 0.459 -.9220572 .4160552
3 -.8029751 .4764975 -1.69 0.092 -1.736893 .1309428
4 -.3197948 .2259747 -1.42 0.157 -.7626972 .1231076
5 -.3627487 .4351505 -0.83 0.404 -1.215628 .4901307
6 -.6737865 .4270767 -1.58 0.115 -1.510841 .1632684
7 -.3036647 .2608696 -1.16 0.244 -.8149597 .2076304
8 -.8162523 .4997311 -1.63 0.102 -1.795707 .1632027
9 -.4992585 .4291815 -1.16 0.245 -1.340439 .3419218

A#B
1 0 0 (empty)
1 1 -1.230081 1.117364 -1.10 0.271 -3.420074 .959912
1 2 .4733394 .7240285 0.65 0.513 -.9457305 1.892409
2 0 0 (empty)
2 1 -.3400353 .5517745 -0.62 0.538 -1.421493 .7414228
2 2 .3566707 .6439284 0.55 0.580 -.9054058 1.618747
3 0 0 (empty)
3 1 .9247375 .7564347 1.22 0.222 -.5578473 2.407322
3 2 .7883511 .603915 1.31 0.192 -.3953006 1.972003
4 0 0 (empty)
4 1 -.6636397 .8769687 -0.76 0.449 -2.382467 1.055187
4 2 .4231619 .6336939 0.67 0.504 -.8188553 1.665179
5 0 0 (empty)
5 1 -.9731552 1.149326 -0.85 0.397 -3.225793 1.279483
5 2 0 (omitted)
6 0 0 (empty)
6 1 0 (omitted)
6 2 .892305 .8134797 1.10 0.273 -.702086 2.486696
7 0 0 (empty)
7 1 -1.088576 .9189969 -1.18 0.236 -2.889777 .7126248
7 2 .579092 .5647428 1.03 0.305 -.5277835 1.685968
8 0 0 (empty)
8 1 .5842016 .9314767 0.63 0.531 -1.241459 2.409862
8 2 .9380063 1.081376 0.87 0.386 -1.181452 3.057465
9 0 0 (empty)
9 1 -.0620931 .849531 -0.07 0.942 -1.727143 1.602957
9 2 .3017286 .5632951 0.54 0.592 -.8023095 1.405767
Thank you!

Connie