I have a Panel dataset with 260,647 data points consisting of 260,647 Respondents within 43,400 households and between 1 and 11 survey waves. Using this dataset, I am trying to run a logistic random effects regression. So far, I am using xtlogit, which works fine except that it doesn't allow me to use the survey weights that are needed to make statistical inference. Additionally, I can't account for the household level in the model, which is why I am currently using robust standard errors. I would prefer to specify a model which allows me to include my survey weight with (pweight) and to explicitly model both the panel structure and the multilevel structure. The model looks like this:
Code:
webuse union.dta, clear
xtset idcode year
xtlogit union, vce(robust) re
(I am using the union data here because I am not allowed to share the actual data that I am using)

The multilevel structure can be added using xtmelogit, but as I understand it, it is no longer part of the official stata which makes me a bit suspicious and also it doesn't allow me to use pweights (and it takes a really long time to run even for the empty model).

A good option seems to be melogit, but for some reason it doesn't converge even when I am using it to fit the empty model without the houdehold level as soon as I include the weights. The model looks like this (of course, there is no weighting variable in the union dataset. To illustrate the problem, I have created a weighting variable pw = 1, but with this, the model runs. So this is just an illustration of what my code looks like and unfortunately can't replicate the problem):
Code:
gen pw = 1
melogit union [pw=pw] || idcode:
With my data, the output from that model looks like this:
Fitting fixed-effects model:

Iteration 0: log likelihood = -4.512e+08
Iteration 1: log likelihood = -4.500e+08
Iteration 2: log likelihood = -4.500e+08
Iteration 3: log likelihood = -4.500e+08

Refining starting values:

Grid node 0: log likelihood = .
Grid node 1: log likelihood = .
Grid node 2: log likelihood = .
Grid node 3: log likelihood = .
(note: Grid search failed to find values that will yield a log likelihood value.)

Fitting full model:

initial values not feasible
r(1400);
Does anyone have an Idea what could be the issue here even without looking at the data? Or maybe there is another way to specify xtlogit-like models in Stata that allows both pweights and another level?