This chapter describes SAS/IML subroutines that are related to univariate, multivariate, and fractional time series analysis and subroutines for Kalman filtering and smoothing. You can use these subroutines to analyze economic and financial time series. You can develop a model of univariate time series and a model of the relationships between vector time series. The Kalman filter subroutines provide analysis of various time series and are presented as a tool for dealing with state space models.
The subroutines offer the following functionality:
generating univariate, multivariate, and fractional time series
computing likelihood function of ARMA, VARMA, and ARFIMA models
computing an autocovariance function of ARMA, VARMA, and ARFIMA models
checking the stationarity of ARMA and VARMA models
filtering and smoothing of time series models by using Kalman filters
fitting time series models, including the AR, periodic AR, time-varying coefficient AR, VAR, and ARFIMA models
handling Bayesian seasonal adjustment models
In addition, SAS/IML software provides decomposition analysis, forecasting of an ARMA model, and fractional differencing of a time series.