LinSDECov {simStateSpace} | R Documentation |
Steady-State Covariance Matrix for the Linear Stochastic Differential Equation Model
Description
The steady-state covariance matrix 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}
.
Usage
LinSDECov(phi, sigma)
Arguments
phi |
Numeric matrix.
The drift matrix
which represents the rate of change of the solution
in the absence of any random fluctuations
( |
sigma |
Numeric matrix.
The covariance matrix of volatility
or randomness in the process
( |
Author(s)
Ivan Jacob Agaloos Pesigan
See Also
Other Simulation of State Space Models Data Functions:
LinSDE2SSM()
,
LinSDEMean()
,
SimBetaN()
,
SimPhiN()
,
SimSSMFixed()
,
SimSSMIVary()
,
SimSSMLinGrowth()
,
SimSSMLinGrowthIVary()
,
SimSSMLinSDEFixed()
,
SimSSMLinSDEIVary()
,
SimSSMOUFixed()
,
SimSSMOUIVary()
,
SimSSMVARFixed()
,
SimSSMVARIVary()
,
TestPhi()
,
TestStability()
,
TestStationarity()
Examples
phi <- matrix(
data = c(
-0.10,
0.05,
0.05,
-0.10
),
nrow = 2
)
sigma <- matrix(
data = c(
2.79,
0.06,
0.06,
3.27
),
nrow = 2
)
LinSDECov(phi = phi, sigma = sigma)