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Kalman Filtering in R

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  • Tusell, Fernando

Abstract

Support in R for state space estimation via Kalman filtering was limited to one package, until fairly recently. In the last five years, the situation has changed with no less than four additional packages offering general implementations of the Kalman filter, including in some cases smoothing, simulation smoothing and other functionality. This paper reviews some of the offerings in R to help the prospective user to make an informed choice.

Suggested Citation

  • Tusell, Fernando, 2011. "Kalman Filtering in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i02).
    • Handle: RePEc:jss:jstsof:v:039:i02
    DOI: http://hdl.handle.net/10.18637/jss.v039.i02
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    References listed on IDEAS

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    1. Siem Jan Koopman & Neil Shephard & Jurgen A. Doornik, 1999. "Statistical algorithms for models in state space using SsfPack 2.2," Econometrics Journal, Royal Economic Society, vol. 2(1), pages 107-160.
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    Cited by:

    1. Petris, Giovanni & Petrone, Sonia, 2011. "State Space Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i04).
    2. Branger, Nicole & Larsen, Linda Sandris & Munk, Claus, 2013. "Robust portfolio choice with ambiguity and learning about return predictability," Journal of Banking & Finance, Elsevier, vol. 37(5), pages 1397-1411.
    3. Eddelbuettel, Dirk & Sanderson, Conrad, 2014. "RcppArmadillo: Accelerating R with high-performance C++ linear algebra," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1054-1063.

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