  The role played by the composite analogue of the log likelihood ratio in
hypothesis testing and in setting confidence regions is not as prominent as it
is in the canonical likelihood setting, since its asymptotic distribution
depends on the unknown parameter. Approximate pivots based on the composite log
likelihood ratio can be derived by using asymptotic arguments. However, the
actual distribution of such pivots may differ considerably from the asymptotic
reference, leading to tests and confidence regions whose levels are distant
from the nominal ones. The use of bootstrap rather than asymptotic
distributions in the composite likelihood framework is explored. Prepivoted
tests and confidence sets based on a suitable statistic turn out to be accurate
and computationally appealing inferential tools.
