Robust inference on parameters via particle filters and sandwich covariance matrices
Neil Shephard, Arnaud Doucet · Oxford University Research Archive (ORA) (University of Oxford) · 2012
Likelihood based estimation of the parameters of state space models can be carried out via a particle filter. In this paper we show how to make valid inference on such parameters when the model is incorrect. In particular we develop a simulation strategy for computing sandwich covariance matrices which can be used for asymptotic likelihood based inference. These methods are illustrated on some simulated data.