The paper introduces four unbiased probability-simulators which produce continuous (simulated) log-likelihood functions with almost everywhere continuous derivatives. Identification conditions are derived which show that in the presence of intercepts in the latent utilities, then the shocks'...

This paper develops an unbiased Monte Carlo approximation to the transition density of a jump-diffusion process with state-dependent drift, volatility, jump intensity, and jump magnitude. The approximation is used to construct a likelihood estimator of the parameters of a jump-diffusion observed...

Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of...