Simulation-based likelihood inference for limited dependent processes

This paper looks at the problem of performing likelihood inference for limited dependent processes. Throughout we use simulation to carry out either classical inference through a simulated score method (simulated EM algorithm) or Bayesian analysis. A common theme is to develop computationally robust methods which are likely to perform well for any time series problem. The central tools we use to deal with the time series dimension of the models are the scan sampler and the simulation signal smoother.