Tempered Particle Filter
Herbst, Edward and Schorfheide, Frank, ‘‘Tempered Particle Filtering.’’ Journal of Econometrics, vol. 210, no. 1, 2019, pp. 26-44., doi:10.1016/j.jeconom.2018.11.003.
The tempered particle filter is a particle filtering method which can approximate the log-likelihood value implied by a general (potentially non-linear) state space system (with potentially non-Gaussian innovations) of the following representation:
General State Space System
s_{t+1} = Phi(s_t, eps_t) (transition equation)
y_t = Psi(s_t) + u_t (measurement equation)
eps_t ~ F_{eps}( . ; 0)
u_t ~ N(0, E)
Cov(eps_t, u_t) = 0
Notice above that unlike the Kalman filter, the tempered particle filter does not impose that one’s model is linear and shocks eps_t are Gaussian.
Documentation and code are located in src/filters/tempered_particle_filter. An example script is located in docs/examples/tempered_particle_filter.
Installation and Versioning
StateSpaceRoutines.jl is a registered Julia package in the
general registry, currently compatible with Julia v1.0 and v1.1. To install, open your Julia REPL, type ] (enter Julia’s package manager), and run
pkg> add StateSpaceRoutines
