Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Rajesh Selukar
Title: State Space Modeling Using SAS
Abstract: This article provides a brief introduction to the state space modeling capabilities in SAS, a well-known statistical software system. SAS provides state space modeling in a few different settings. SAS/ETS, the econometric and time series analysis module of the SAS system, contains many procedures that use state space models to analyze univariate and multivariate time series data. In addition, SAS/IML, an interactive matrix language in the SAS system, provides Kalman filtering and smoothing routines for stationary and nonstationary state space models. SAS/IML also provides support for linear algebra and nonlinear function optimization, which makes it a convenient environment for general-purpose state space modeling.

Page views:: 11101. Submitted: 2009-12-23. Published: 2011-05-12.
Paper: State Space Modeling Using SAS     Download PDF (Downloads: 12933)
Supplements: SAS example code from the paper Download (Downloads: 1368; 16KB)
v41i12.lst: SAS output for examples from the paper Download (Downloads: 1319; 17KB)

DOI: 10.18637/jss.v041.i12

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.