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State Space Modeling Using SAS

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  • Selukar, Rajesh

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.

Suggested Citation

  • Selukar, Rajesh, 2011. "State Space Modeling Using SAS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i12).
    • Handle: RePEc:jss:jstsof:v:041:i12
    DOI: http://hdl.handle.net/10.18637/jss.v041.i12
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    Cited by:

    1. Yue Zhao & Difang Wan, 2018. "Institutional high frequency trading and price discovery: Evidence from an emerging commodity futures market," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(2), pages 243-270, February.
    2. Diego J Pedregal, 2019. "Time series analysis and forecasting with ECOTOOL," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-23, October.

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