Previous Page | Next Page

Introduction

SAS/IML Software

SAS/IML software gives you access to a powerful and flexible programming language (Interactive Matrix Language) in a dynamic, interactive environment. The fundamental object of the language is a data matrix. You can use SAS/IML software interactively (at the statement level) to see results immediately, or you can store statements in a module and execute them later. The programming is dynamic because necessary activities such as memory allocation and dimensioning of matrices are done automatically.

You can access built-in operators and call routines to perform complex tasks such as matrix inversion or eigenvector generation. You can define your own functions and subroutines using SAS/IML modules. You can perform operations on an entire data matrix. You have access to a wide choice of data management commands. You can read, create, and update SAS data sets from inside SAS/IML software without ever using the DATA step.

SAS/IML software is of interest to users of SAS/ETS software because it enables you to program your own econometric and time series methods in the SAS System. It contains subroutines for time series operators and for general function optimization. If you need to perform a statistical calculation not provided as an automated feature by SAS/ETS or other SAS software, you can use SAS/IML software to program the matrix equations for the calculation.

Kalman Filtering and Time Series Analysis in SAS/IML

SAS/IML software includes CALL routines and functions for Kalman filtering and time series analysis, which perform the following:

  • generate univariate, multivariate, and fractional time series

  • compute likelihood function of ARMA, VARMA, and ARFIMA models

  • compute an autocovariance function of ARMA, VARMA, and ARFIMA models

  • check the stationarity of ARMA and VARMA models

  • filter and smooth time series models using Kalman method

  • fit AR, periodic AR, time-varying coefficient AR, VAR, and ARFIMA models

  • handle Bayesian seasonal adjustment models

Previous Page | Next Page | Top of Page