parmaresid {perARMA} | R Documentation |
Computing residuals of PARMA series
Description
Procedure parmaresid
, given phi
(of size T \times p
), del
(of size T \times 1
),
theta
(of size T \times q
), computes the residuals of PARMA series.
Usage
parmaresid(x, stype, del, phi,...)
Arguments
x |
input time series. |
stype |
numeric parameter connected with covariance matrix computation, so far should be equal to 0 to use procedure
|
del |
vector of coefficients of length |
phi |
matrix of coefficients of size |
... |
matrix of coefficients |
Details
This program uses parmafil
to filter the series and computes the covariance matrix.
This code does the Cholesky factorization and
determines the residuals from the inverse of L
(see the code:
e=Linv*w0_r1
). This allows the treatment of a deficient rank
covariance and a reduction of rank.
Procedure parmaresid
is used in parmaf
function.
Value
Series of residuals resids
.
Author(s)
Harry Hurd
References
Box, G. E. P., Jenkins, G. M., Reinsel, G. (1994), Time Series Analysis, 3rd Ed., Prentice-Hall,
Englewood Cliffs, NJ.
Brockwell, P. J., Davis, R. A. (1991), Time Series: Theory and Methods, 2nd Ed., Springer: New York.
Vecchia, A., (1985), Maximum Likelihood Estimation for Periodic Autoregressive Moving Average Models, Technometrics, v. 27, pp.375-384.
See Also
Examples
## Do not run
## It could take a few seconds
data(volumes)
pmean<-permest(t(volumes),24, 0.05, NaN,'volumes', pp=0)
xd=pmean$xd
estimators<-perYW(volumes,24,2,NaN)
parmaresid(xd, 0, estimators$del, estimators$phi)