  Maximum composite likelihood estimation is a useful alternative to maximum
likelihood estimation when data arise from data generating processes (DGPs)
that do not admit tractable joint specification. We demonstrate that generic
composite likelihoods consisting of marginal and conditional specifications
permit the simple construction of composite likelihood ratio-like statistics
from which finite-sample valid confidence sets and hypothesis tests can be
constructed. These statistics are universal in the sense that they can be
constructed from any estimator for the parameter of the underlying DGP. We
demonstrate our methodology via a simulation study using a pair of
conditionally specified bivariate models.
