Wald test: alternatives and small sample sizes

["Collewaert V (MCFE)" <v.collewaert@maastrichtuniversity.nl>]

Dear Statalist,

I am trying to estimate two models (on two subsamples) with SuEst and cluster option as both samples are related (they belong to the same ventures). Specifically:

Regress Y X Y Z + controls if group = 1
Est store one
Regress Y X Y Z + controls if group = 0
Est store two
Suest one two, Cluster(Nr_Co)

However (!) the control variables are different for each group (for instance I control for experience in group 1, but not in group 0, and control for tenure in group 0, but not in group 1), so I do not have the same model for both groups.

X, Y and Z refer to three main constructs of interest to my study and are included in both models. One of my hypotheses is that construct X should have a stronger (and positive) effect on group 1's outcome than on group 0's outcome. I tried running a Wald test:

Test [one_mean = two_mean] X

However, results seem strange to me: X is highly significant in model (group) 1, but absolutely not significant in model (group) 2 and still the Wald test proclaims that both coefficients are equal (chi2(  1) =    1.09,  Prob > chi2 =    0.2966). Could the problem be my small sample sizes? (respectively 72 and 65) And if so, what alternatives could I try? Or should I use another test than the Wald test to test this kind of hypothesis?

With kind regards,

Veroniek






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