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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 with 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 High-Performance Forecasting 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 High-Performance Forecasting 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 a library for Kalman filtering and time series analysis. The library provides the following functions:

  • 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 with the Kalman method

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

  • handling Bayesian seasonal adjustment model

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