A Tutorial on Particle Filters: Filters, Smoothing, and Parameter Estimation

Abstract

Particle filters and smoothers are simulation-based methods to estimate non-linear non-Gaussian state space models. The filters and smoothers are widely applied to science and engineering from the early 1990s. We describe an introduction to the particle filter and some applications in Section 2. The particle fixed-lag smoother is denoted in Section 3, and we apply the resample-move method to the particle fixed-lag smoother in Section 4. We explain parameter estimation and a self-organzing state space model in Section 5. In Section 6, we estimate a Real Business Cycle model based on the filter.