Language Reference |
ARMACOV Call |
The ARMACOV subroutine computes an autocovariance sequence for an autoregressive moving average (ARMA) model. The input arguments to the ARMACOV subroutine are as follows:
refers to a matrix that contains the autoregressive parameters. The first element is assumed to have the value 1.
refers to a matrix that contains the moving average parameters. The first element is assumed to have the value 1.
refers to a scalar that contains , the number of autocovariances to be computed, which must be a positive number.
The ARMACOV subroutine returns the following values:
specifies a variable to contain the returned matrix that contains the autocovariances of the specified ARMA model, assuming unit variance for the innovation sequence.
specifies a variable to contain the returned matrix that contains the covariances of the moving-average term with lagged values of the process.
specifies a variable to contain the returned matrix that contains the autocovariance sequence of the moving-average term.
The ARMACOV subroutine computes the autocovariance sequence that corresponds to a given autoregressive moving-average (ARMA) time series model. An arbitrary number of terms in the sequence can be requested. Two related covariance sequences are also returned.
The model notation for the ARMACOV and ARMALIK subroutines is the same. The ARMA model is denoted
with . The notation is the same as that of Box and Jenkins (1976) except that the model parameters are opposite in sign. The innovations satisfy and if = 0, and are zero otherwise. The formula for the th element of the convol argument is
for . The formula for the th element of the cross argument is
for , where is the th impulse response value. The sequence, if desired, can be computed with the RATIO function. It can be shown that is the same as , which is used by Box and Jenkins (1976) in their formulation of the autocovariances. The th autocovariance, denoted and returned as the element of the auto argument , is defined implicitly for by
where is the th element of the cross argument. See Box and Jenkins (1976) or McLeod (1975) for more information.
Consider the model
To compute the autocovariance function at lags zero through four for this model, use the following statements:
/* an ARMA(1,1) model */ phi = {1 -0.5}; theta = {1 0.8}; call armacov(auto, cross, convol, phi, theta, 5); print auto, cross convol;
The result is show in Figure 23.41.
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