Vector Time Series Analysis Subroutines

Vector time series analysis involves more than one dependent time series variable, with possible interrelations or feedback between the dependent variables.

The VARMASIM function generates various time series from the underlying VARMA models. Simulations of time series with known VARMA structure offer learning and developing vector time series analysis skills.

The VARMACOV subroutine provides the pattern of the autocovariance function of VARMA models and helps to identify and fit a proper model.

The VARMALIK subroutine provides the log-likelihood of a VARMA model and helps to obtain estimates of the parameters of a regression model.

The following subroutines are supported:

VARMACOV

computes the theoretical cross covariances for a multivariate ARMA model

VARMALIK

evaluates the log-likelihood function for a multivariate ARMA model

VARMASIM

generates a multivariate ARMA time series

VNORMAL

generates a multivariate normal random series

VTSROOT

computes the characteristic roots of a multivariate ARMA model