sim.varma {ldt} | R Documentation |
Generate Random Sample from a VARMA Model
Description
This function generates a multivariate time series using a VARMA process.
Usage
sim.varma(
sigma = 2L,
arList = 1L,
maList = 0L,
exoCoef = 0L,
nObs = 100,
nBurn = 10,
intercept = TRUE,
d = 0,
startFrequency = NULL,
seasonalCoefs = NULL
)
Arguments
sigma |
A positive definite matrix representing the covariance matrix of the white noise series or an integer representing the dimension of a random covariance matrix to generate. |
arList |
A list of matrices representing the AR coefficients of the VARMA model or an integer representing the number of random AR coefficients to generate. |
maList |
A list of matrices representing the MA coefficients of the VARMA model or an integer representing the number of random MA coefficients to generate. For identification purposes, it generates diagonal matrices. |
exoCoef |
A matrix representing the coefficients of the exogenous variables or an integer representing the number of random exogenous coefficients to generate. |
nObs |
An integer representing the number of observations to generate. |
nBurn |
An integer representing the number of burn-in observations to remove from the generated time series. |
intercept |
A numeric vector representing the intercept of the VARMA model or a logical value indicating whether to generate a random intercept. |
d |
An integer representing the order of integration. |
startFrequency |
The frequency of the first observation in the data. |
seasonalCoefs |
An integer vector of size 4: |
Value
A list with the following items:
y |
The simulated endogenous data. |
x |
The simulated exogenous data. |
e |
The simulated white noise series. |
sigma |
The covariance matrix of the white noise series. |
arList |
The list of autoregressive coefficients. |
maList |
The list of moving average coefficients. |
exoCoef |
The matrix of exogenous coefficients. |
intercept |
The intercept vector. |
d |
The order of the integration. |
seasonalCoefs |
The argument |
nObs |
The number of observations generated. |
nBurn |
The number of burn-in observations removed. |
Examples
sample1 <- sim.varma(2L, 3L, 2L)
ar1 <- matrix(c(0.7,0.2,-0.4,0.3),2,2)
ar2 <- matrix(c(-0.4,0.1,0.2,-0.3),2,2)
ma1 <- matrix(c(0.5,-0.1,0.3,0.4),2,2)
Sigma <- matrix(c(1,0.3,0.3,1),2,2)
B <- matrix(c(0.5,-0.3),2)
sample2 <- sim.varma(Sigma, list(ar1, ar2), list(ma1), exoCoef = B ,
nObs =100, nBurn =10 , intercept = c(1,-1))
# Plot the y series
matplot(sample2$y,type = "l")
# see the examples in 'estim.varma' or 'search.varma' functions