I'm running a 4-equation model (2 x Heckman selection model) with CMP and I want to add random intercepts (maybe later also random slopes). Without any random effects, the model runs fast (4 hours for a N = 700,000 dataset and ~ 10 predictors per equation). I'm running the model on a Linux server and it consumes at maximum 2GB of RAM.

When I start adding a random intercept (the data belongs to 17,000 individuals) to each of the four equations, after a few minutes, Stata consumes more than 500GB of RAM (which is the maximum I get allocate to the job) and it aborts the estimation.

I've tried adding random intercepts to only two out of the four equations, then the model consumes at maximum 22GB of RAM but has not finished yet after 3.5 days.

I'm wondering why adding the random intercepts has such an enormous effect on the computational effort of the model.

Any ideas what's going on?