TSPRED Call
provides predicted values of univariate and multivariate
ARMA processes when the ARMA coefficients are input
- CALL TSPRED( forecast, impulse, mse, data, coef, nar, nma
- <,ev, npred, start,
constant>);
The inputs to the TSPRED subroutine are as follows:
- data
- specifies a T ×M data matrix if the intercept is
not included, where T denotes the length of the time
series and M is the number of variables to be analyzed.
If the univariate time series is analyzed,
the input data should be a column vector.
- coef
- refers to the M(P+Q) ×M ARMA coefficient matrix,
where P is an AR order and Q is an MA order.
If the intercept term is included (constant=1), the first
row of the coefficient matrix is considered as the intercept
term and the coefficient matrix is an M(P+Q+1) ×M matrix.
If there are missing values in the coef
matrix, these are converted to zero.
- nar
- specifies the order of the AR process.
If the subset AR process is requested,
nar should be a row or column vector.
The default is nar=0.
- nma
- specifies the order of the MA process.
If the subset MA process is requested,
nma should be a vector.
The default is nma=0.
- ev
- specifies the error variance matrix.
If the ev matrix is not provided, the
prediction error covariance will not be computed.
- npred
- specifies the maximum length of multistep forecasting.
The default is npred=0.
- start
- specifies the position where the multistep forecast starts.
The default is start=n.
- constant
- specifies the intercept option.
No intercept estimate is included if constant=0;
otherwise, the intercept estimate is included
in the first row of the coefficient matrix.
If constant=-1, the coefficient matrix
is estimated by using mean deleted series.
By default, constant=0.
The TSPRED subroutine returns the following values:
- forecast
- refers to predicted values.
- impulse
- refers to the impulse response function.
- mse
- refers to the mean square error of s-step-ahead forecast.
A scalar missing value is returned if the
error variance (ev) is not provided.
Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
