Steady-State Covariance Matrix for the Latent Variables in the Linear Stochastic Differential Equation Model
Source:R/simStateSpace-lin-sde-cov-eta.R
LinSDECovEta.Rd
The steady-state covariance matrix for the latent variables in the linear stochastic differential equation model \(\mathrm{Cov} \left( \boldsymbol{\eta} \right)\) is the solution to the Sylvester equation, i.e. $$ \mathbf{A} \mathbf{X} + \mathbf{X} \mathbf{B} + \mathbf{C} = \mathbf{0} , $$ where \(\mathbf{X}\) is unknown, \(\mathbf{A} = \boldsymbol{\Phi}\), \(\mathbf{B} = \boldsymbol{\Phi}^{\prime}\), and \(\mathbf{C} = \boldsymbol{\Sigma}\) where \(\boldsymbol{\Phi}\) is the drift matrix and \(\boldsymbol{\Sigma}\) is the covariance matrix of volatility or randomness in the process.
See also
Other Simulation of State Space Models Data Functions:
LinSDE2SSM()
,
LinSDECovY()
,
LinSDEMeanEta()
,
LinSDEMeanY()
,
SSMCovEta()
,
SSMCovY()
,
SSMMeanEta()
,
SSMMeanY()
,
SimAlphaN()
,
SimBetaN()
,
SimCovDiagN()
,
SimCovN()
,
SimIotaN()
,
SimPhiN()
,
SimSSMFixed()
,
SimSSMIVary()
,
SimSSMLinGrowth()
,
SimSSMLinGrowthIVary()
,
SimSSMLinSDEFixed()
,
SimSSMLinSDEIVary()
,
SimSSMOUFixed()
,
SimSSMOUIVary()
,
SimSSMVARFixed()
,
SimSSMVARIVary()
,
TestPhi()
,
TestStability()
,
TestStationarity()
Examples
phi <- matrix(
data = c(
-0.357, 0.771, -0.450,
0.0, -0.511, 0.729,
0.0, 0.0, -0.693
),
nrow = 3
)
sigma <- matrix(
data = c(
0.24455556, 0.02201587, -0.05004762,
0.02201587, 0.07067800, 0.01539456,
-0.05004762, 0.01539456, 0.07553061
),
nrow = 3
)
LinSDECovEta(
phi = phi,
sigma = sigma
)
#> [,1] [,2] [,3]
#> [1,] 0.3425148 0.3296023 0.0343817
#> [2,] 0.3296023 0.5664625 0.2545955
#> [3,] 0.0343817 0.2545955 0.2999909