On the occasional exactness of the distributional transform approximation for direct Gaussian copula models with discrete margins

The direct Gaussian copula model with discrete margins is appealing but poses computational challenges due to its intractable likelihood. We show that the distributional transform-based approximate likelihood is essentially exact for some variants of the model, and we propose a quantity that can be used to assess exactness for a given dataset.