Language Reference: TSPRED Call

TSPRED Call

CALL TSPRED( forecast, impulse, mse, data, coef, nar, nma <*>, ev <*>, npred <*>, start <*>, constant ) ;

The TSPRED subroutine provides predicted values of univariate and multivariate ARMA processes when the ARMA coefficients are input.

The input arguments to the TSPRED subroutine are as follows:

data: specifies a $\text{[math]}$ data matrix if the intercept is not included, where $\text{[math]}$ denotes the length of the time series and $\text{[math]}$ 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 $\text{[math]}$ ARMA coefficient matrix, where $\text{[math]}$ is an AR order and $\text{[math]}$ 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 $\text{[math]}$ 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= $\text{[math]}$ .
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= $\text{[math]}$ , 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 $\text{[math]}$ -step-ahead forecast. A scalar missing value is returned if the error variance (ev) is not provided.