Hello,
Thank you for reading my post. I am seeking advice about the following problem:

I am estimating a model with the melogit command and I am encountering an error. The issue arises when I specify a random slope on a dummy variable that does not vary within groups (only across groups). Stata produces a "." for the confidence intervals on the variance of the dummy variable in my random effects equation. When I attempt a likelihood ratio test of the random slope/random intercept model against a simpler random intercept model, Stata cannot compute the test.

Can anyone tell me why this is happening? Is it not possible to specify a random slope on a dummy variable that does not vary within groups? (For context, the variable represents the ideology of a political organization, which does not change over the life of the group, only across different groups.)

A simplified version of the model and output are below. x3 is the dummy variable in question.


Command entered:

melogit dep_var x1 x2 x3 ... xn || group_var: x3 , difficult

Result:

Mixed-effects logistic regression Number of obs = 9,403
Group variable: group_var Number of groups = 111

Obs per group:
min = 1
avg = 70.3
max = 1,602

Integration method: mvaghermite Integration pts. = 7

Wald chi2(16) = 122.15
Log likelihood = -1429.8005 Prob > chi2 = 0.0000
----------------------------------------------------------------------------------
dep_var | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-----------------+----------------------------------------------------------------
x1 | -.9199624 .2245428 -4.10 0.000 -1.360058 -.4798666
x2 | .2273233 .258696 0.88 0.380 -.2797115 .7343581
x3 | -.4584869 .2303522 -1.99 0.047 -.9099688 -.0070049

[some variables not shown]

xn | 3.570397 .5860844 6.09 0.000 2.421693 4.719101
_cons | -5.265287 .7204782 -7.31 0.000 -6.677399 -3.853176
-----------------+----------------------------------------------------------------
group-var |
var(x3) | 1.28e-28 1.96e-14 . .
var(_cons)| 2.575771 .6452784 1.576396 4.20871
---------------------------------------------------------------------------------
LR test vs. logistic model: chibar2(01) = 424.62 Prob >= chibar2 = 0.0000