As seen in the previous section, there is currently no general method for generating confidence intervals for models with constrained parameters with correct size using non-Bayesian methods. This conclusion is not modified for the simple bootstrap (Andrews, 1997). Newton and Raftery (1994), however, describe a weighted bootstrap that incorporates a prior distribution of the parameters that generates a simulated posterior distribution of the parameters for constrained models that appears to have correct size.
The weighted likelihood bootstrap is presented as an alternative to the Markov chain Monte Carlo methods (Geweke, 1995) for the model with constrained parameters