# Joint likelihood of two models

I am estimating a hurdle model, which consists of two data-generating processes: one for the zero/positive relationship, and a different one for the positive numbers above zero. A binary logit model is often used for the first part, and a zero-truncated poisson or negative binomial model for the second.

In R, the hurdle() command in the pscl package jointly estimates the models together, and returns a single AIC and log-likelihood value. I need to incorporate some random effects, and therefore am using the glmmADMB() command in the package of the same name. This package does not have a single hurdle model command, so I must estimate them separately (first binary, then poisson).

My question: how should one calculate the joint log-likelihood of two separately estimated models? I assume this is possible, given that hurdle seems to do just this.

## 1 Answer

So it turns out the solution is trivial, and is simply the sum of the two sub-models.