/StateSpaceRoutines.jl

Package implementing common state-space routines.

Primary LanguageJuliaBSD 3-Clause "New" or "Revised" LicenseBSD-3-Clause

State-Space Routines

Build Status codecov

This package implements some common routines for state-space models. Provided algorithms include:

Installation

StateSpaceRoutines.jl is a registered Julia package in the General registry. To install, open your Julia REPL, type ] (enter package manager), and run

pkg> add StateSpaceRoutines

Versioning

StateSpaceRoutines.jl is currently compatible with Julia 1.x.

To use StateSpaceRoutines.jl with Julia v0.7, please check out tag 0.2.0. To do this, click on the drop-down menu that reads branch: main on the left-hand side of the page. Select tags, then v0.2.0. If you've already cloned the repo, you can simply run git checkout v0.2.0.

Precompilation

The StateSpaceRoutines.jl package is not precompiled by default because when running code in parallel, we want to re-compile the copy of StateSpaceRoutines.jl on each processor to guarantee the right version of the code is being used. If users do not anticipate using parallelism, then users ought to change the first line of src/StateSpaceRoutines.jl from

isdefined(Base, :__precompile__) && __precompile__(false)

to

isdefined(Base, :__precompile__) && __precompile__(true)

Linear Estimation

Linear State Space System

s_{t+1} = C + T*s_t + R*ϵ_t    (transition equation)
y_t     = D + Z*s_t + u_t     (measurement equation)

ϵ_t ∼ N(0, Q)
u_t ∼ N(0, E)
Cov(ϵ_t, u_t) = 0

Time-Invariant Methods

kalman_filter(y, T, R, C, Q, Z, D, E, s_0 = Vector(), P_0 = Matrix(); outputs = [:loglh, :pred, :filt], Nt0 = 0)
chand_recursion(y, T, R, C, Q, Z, D, E, s_pred = Vector(), P_pred = Matrix(); allout = false, Nt0 = 0, tol = 0.0)
tempered_particle_filter(y, Φ, Ψ, F_ϵ, F_u, s_init; verbose = :high, include_presample = true, fixed_sched = [], r_star = 2, c = 0.3, accept_rate = 0.4, target = 0.4, xtol = 0, resampling_method = :systematic, N_MH = 1, n_particles = 1000, Nt0 = 0, adaptive = true, allout = true, parallel = false)

hamilton_smoother(y, T, R, C, Q, Z, D, E, s_0, P_0; Nt0 = 0)
koopman_smoother(y, T, R, C, Q, Z, D, s_0, P_0, s_pred, P_pred; Nt0 = 0)
carter_kohn_smoother(y, T, R, C, Q, Z, D, E, s_0, P_0; Nt0 = 0, draw_states = true)
durbin_koopman_smoother(y, T, R, C, Q, Z, D, E, s_0, P_0; Nt0 = 0, draw_states = true)

For more information, see the docstring for each function (e.g. enter ?kalman_filter in the REPL).

Regime-Switching Methods

All of the provided algorithms can handle time-varying state-space systems. To do this, define regime_indices, a Vector{Range{Int64}} of length n_regimes, where regime_indices[i] indicates the range of periods t in regime i. Let T_i, R_i, etc. denote the state-space matrices in regime i. Then the state space is given by:

s_{t+1} = C_i + T_i*s_t + R_i*ϵ_t    (transition equation)
y_t     = D_i + Z_i*s_t + u_t        (measurement equation)

ϵ_t ∼ N(0, Q_i)
u_t ∼ N(0, E_i)

Letting Ts = [T_1, ..., T_{n_regimes}], etc., we can then call the time-varying methods of the algorithms:

kalman_filter(regime_indices, y, Ts, Rs, Cs, Qs, Zs, Ds, Es, s_0 = Vector(), P_0 = Matrix(); outputs = [:loglh, :pred, :filt], Nt0 = 0)

hamilton_smoother(regime_indices, y, Ts, Rs, Cs, Qs, Zs, Ds, Es, s_0, P_0; Nt0 = 0)
koopman_smoother(regime_indices, y, Ts, Rs, Cs, Qs, Zs, Ds, s_0, P_0, s_pred, P_pred; Nt0 = 0)
carter_kohn_smoother(regime_indices, y, Ts, Rs, Cs, Qs, Zs, Ds, Es, s_0, P_0; Nt0 = 0, draw_states = true)
durbin_koopman_smoother(regime_indices, y, Ts, Rs, Cs, Qs, Zs, Ds, Es, s_0, P_0; Nt0 = 0, draw_states = true)

Nonlinear Estimation

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 the following representation:

General State Space System

s_{t+1} = Φ(s_t, ϵ_t)        (transition equation)
y_t     = Ψ(s_t) + u_t       (measurement equation)

ϵ_t ∼ F_ϵ(∙; θ)
u_t ∼ N(0, E)
Cov(ϵ_t, u_t) = 0

Disclaimer

Copyright Federal Reserve Bank of New York. You may reproduce, use, modify, make derivative works of, and distribute and this code in whole or in part so long as you keep this notice in the documentation associated with any distributed works. Neither the name of the Federal Reserve Bank of New York (FRBNY) nor the names of any of the authors may be used to endorse or promote works derived from this code without prior written permission. Portions of the code attributed to third parties are subject to applicable third party licenses and rights. By your use of this code you accept this license and any applicable third party license.

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