Bayesian State-Space Model
A Bayesian state-space model treats the linear, Gaussian state-space model parameters θ as random variables, rather than fixed but unknown quantities, with joint prior distribution Π(θ). This treatment leads to a more flexible model and intuitive inferences.
Bayesian state-space model analyses involve drawing samples from the joint posterior distribution Π(θ|Dt), which is composed of the joint prior and data likelihood computed by the standard Kalman filter, where Dt is the response and predictor data set. Econometrics Toolbox™ uses a Metropolis-Hastings sampler to sample from the posterior.
To start a Bayesian state-space model analysis, create a model object
that best describes the structure of the state-space (from which the
likelihood is inferred) and your prior assumptions on the joint
distribution of the parameters by using
bssm. Then, using the model and data, you can estimate
characteristics of the posterior distributions or draw samples from the