WORK IN PROGRESS

KalmanFilterTools provides efficient code to perform various computations pertaining to state space models and the Kalman Filter, such as the Kalman filter proper, the Kalman smoother or computing the log likelihood for the model.

Because such operations are very often computed in an iterative manner, all operations are computed /in place/. One function allocate the necessary workspace and another function performs the computations.

julia> using Pkg
julia> Pkg.add("KalmanFilterTools")

KalmanFilterTools requires Julia version >= 1.4

KalmanFilterTools handles state space models of the following form:

  y_t = Z a_t + \epsilon_t
  a_{t+1} = Ta_t + R\eta_t

  \epsilon_t \sim N(0,H)
  \eta_t \sim N(0,Q)

y_t: observation vector ny x 1 a_t: state vector ns x 1 \epsilon_t: measurement error vector ny x 1 \eta_t: shocks vector np x 1 Z: ny x ns matrix T: ns x ns matrix R: ns x np matrix H: ny x ny covariance matrix Q: ns x ns covariance matrix

Computing the log likelihood

 using KalmanFilterTools

 data = ....
 Z = ...
 T = ...
 R = ...
 Q = ...
 a = ...
 P = ...
 
 ny, ns = size(Z)
 np = size(R, 2)
 nobs = size(data,2)
 first_obs = 1
 last_obs = nobs
 presample = 0
 
 kalman_ws = KalmanLikelihoodWs{Float64, Integer}(ny, ns, np, nobs)

 llk = kalman_likelihood(data, Z, H, T, R, Q, a, P, first_obs, last_obs, presample, kalman_ws)