Vector time series analysis involves more than one dependent time series variable, with possible interrelations or feedback between the dependent variables.
The VARMASIM subroutine generates various time series from the underlying VARMA models. Simulation of time series that have a known VARMA structure enables you to develop analytical skills for vector time series.
The VARMACOV subroutine provides the pattern of the autocovariance function of VARMA models and helps you identify and fit a proper model.
The VARMALIK subroutine provides the log likelihood of a VARMA model and helps you obtain estimates of the parameters of a regression model.
The following subroutines are supported:
computes the theoretical cross covariances for a multivariate ARMA model.
evaluates the log-likelihood function for a multivariate ARMA model.
generates a multivariate ARMA time series.
generates a multivariate normal random series.
computes the characteristic roots of a multivariate ARMA model.