Model Parameters

Usage

data(model)

A list with 15 elements:

k: Number of variables.
mu_mu: Mean of the set-point vector \(\boldsymbol{\mu}\).
mu_sigma: Covariance matrix of the parameter \(\boldsymbol{\mu}\).
mu_sigma_l: Cholesky factor of the covariance matrix of the parameter \(\boldsymbol{\mu}\).
beta_mu: Mean of lagged coefficients matrix \(\boldsymbol{\beta}\).
beta_sigma: Covariance matrix of the parameter \(\mathrm{vec} \left( \boldsymbol{\beta} \right)\).
beta_sigma_l: Cholesky factor of the covariance matrix of the parameter \(\mathrm{vec} \left( \boldsymbol{\beta} \right)\).
psi: Process noise covariance matrix \(\boldsymbol{\Psi}\).
psi_l: Cholesky factor of the process noise covariance matrix \(\boldsymbol{\Psi}\).
psi_d_ldl: uc_d of the LDL' decomposition of the process noise covariance matrix \(\boldsymbol{\Psi}\). See fitVARMxID::LDL().
psi_l_ldl: s_l of the LDL' decomposition of the process noise covariance matrix \(\boldsymbol{\Psi}\). See fitVARMxID::LDL().
ma_fixed: Vector of fixed effects \( \boldsymbol{\theta} = \left[ \boldsymbol{\mu}, \mathrm{vec} \left( \boldsymbol{\beta} \right) \right]^{\prime} \)
ma_random: Matrix of random effects \( \boldsymbol{\theta} = \left[ \boldsymbol{\mu}, \mathrm{vec} \left( \boldsymbol{\beta} \right) \right]^{\prime} \)
ma_random_d_ldl: uc_d of the LDL' decomposition of the random effects. See fitVARMxID::LDL().
ma_random_l_ldl: s_l of the LDL' decomposition of the random effects. See fitVARMxID::LDL().

Ivan Jacob Agaloos Pesigan