Particle swarm optimization particle filtering for dual estimation

A new method for the dual estimation in state-space dynamic model was described, with a focus on sequential Bayesian learning about time-varying state and static parameters simultaneously. The new algorithm combines auxiliary particle filtering (APF) with particle swarm optimization (PSO) to achieve computational efficiency and stability. By properly choosing the fitness function of PSO, the algorithm produces the recursive maximum-likelihood estimation of the parameters. It is shown that the 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.