Construct an object of class gssm by defining the corresponding terms
of the observation and state equation:
mv_gssm(y, Z, H, T, R, a1, P1, xreg = NULL, beta, state_names, H_prior,
Z_prior, T_prior, R_prior, obs_intercept, state_intercept)Observations as multivariate time series (or matrix) of length \(n\).
System matrix Z of the observation equation. Either a p x m matrix or a p x m x n array, or an object which can be coerced to such.
Covarianc matrix for observational level noise.
System matrix T of the state equation. Either a m x m matrix or a m x m x n array, or object which can be coerced to such.
Lower triangular matrix R the state equation. Either a m x k matrix or a m x k x n array, or object which can be coerced to such.
Prior mean for the initial state as a vector of length m.
Prior covariance matrix for the initial state as m x m matrix.
An array containing p covariate matrices with dimensions n x h.
matrix of regression coefficients with n columns. Used as an initial value in MCMC. Defaults to matrix of zeros.
Names for the states.
Priors for the NA values in system matrices.
Intercept terms for observation and state equations, given as a p times n and m times n matrices.
Object of class mv_gssm.
$$y_t = D_t + Z_t \alpha_t + H_t \epsilon_t, (\textrm{observation equation})$$ $$\alpha_{t+1} = C_t + T_t \alpha_t + R_t \eta_t, (\textrm{transition equation})$$
where \(\epsilon_t \sim N(0, I_p)\), \(\eta_t \sim N(0, I_k)\) and \(\alpha_1 \sim N(a_1, P_1)\) independently of each other.