Create a DLM representation of an ARMA process
The function creates an object of class dlm representing a specified univariate or multivariate ARMA process
dlmModARMA(ar = NULL, ma = NULL, sigma2 = 1, dV, m0, C0)
ar |
a vector or a list of matrices (in the multivariate case) containing the autoregressive coefficients. |
ma |
a vector or a list of matrices (in the multivariate case) containing the moving average coefficients. |
sigma2 |
the variance (or variance matrix) of the innovations. |
dV |
the variance, or the diagonal elements of the variance
matrix in the multivariate case, of the observation noise. |
m0 |
m0, the expected value of the pre-sample state vector. |
C0 |
C0, the variance matrix of the pre-sample state vector. |
The returned DLM only gives one of the many possible representations of an ARMA process.
The function returns an object of class dlm representing the ARMA
model specified by ar, ma, and sigma2.
Giovanni Petris GPetris@uark.edu
Giovanni Petris (2010), An R Package for Dynamic Linear
Models. Journal of Statistical Software, 36(12), 1-16.
http://www.jstatsoft.org/v36/i12/.
Petris, Petrone, and Campagnoli, Dynamic Linear Models with
R, Springer (2009).
Durbin and Koopman, Time series analysis by state space
methods, Oxford University Press, 2001.
## ARMA(2,3)
dlmModARMA(ar = c(.5,.1), ma = c(.4,2,.3), sigma2=1)
## Bivariate ARMA(2,1)
dlmModARMA(ar = list(matrix(1:4,2,2), matrix(101:104,2,2)),
ma = list(matrix(-4:-1,2,2)), sigma2 = diag(2))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.