In classical linear regression analysis, the underlying process often can be represented simply by an intercept and slope parameters. A time series can be modeled by a type of regression analysis.
The ARMASIM function generates various time series from the underlying AR, MA, and ARMA models. Simulations of time series with known ARMA structure are often needed as part of other simulations or as learning data sets for developing time series analysis skills.
The ARMACOV subroutine provides the pattern of the autocovariance function of AR, MA, and ARMA models and helps to identify and fit a proper model.
The ARMALIK subroutine provides the log likelihood of an ARMA model and helps to obtain estimates of the parameters of a regression model with innovations having an ARMA structure.
The following subroutines and functions are supported:
computes an autocovariance sequence for an ARMA model.
computes the log likelihood and residuals for an ARMA model.
simulates an ARMA series.
See the examples of the use of ARMACOV and ARMALIK subroutines in Chapter 9.