Particle swarm optimisation particle filtering for dual estimation


A new method for the dual estimation in dynamic state-space model was proposed in this study with a focus on sequential Bayesian learning about time-varying state and static parameter simultaneously. The proposed algorithm combines auxiliary particle filtering (APF) with particle swarm optimisation (PSO) to achieve computational efficiency and stability. The PSO provides the mechanism for generating new parameter values for the particle filtering at each time step. By properly choosing the fitness function of PSO, the algorithm produces the recursive maximum-likelihood estimation of the parameter. It is shown that PSO can be integrated with APF in the simulation-based sequential frame for dual estimation. The algorithm is tested on Markov switching stochastic volatility model with promising results compared with existing ones.

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