Robust inference on parameters via particle filters and sandwich covariance matrices

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.

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Year of publication:	2012-06-01
Authors:	Shephard, Neil ; Doucet, Arnaud
Institutions:	Department of Economics, Oxford University
Subject:	Quasi-likelihood \| Particle filter \| Sandwich matrix \| Sequential Monte Carlo

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Extent:	application/pdf
Series:	Economics Series Working Papers.
Type of publication:	Book / Working Paper
Notes:	Number 606
Classification:	C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods ; C53 - Forecasting and Other Model Applications ; c58
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10011004407