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State Space Models in R

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  • Petris, Giovanni
  • Petrone, Sonia

Abstract

We give an overview of some of the software tools available in R, either as built- in functions or contributed packages, for the analysis of state space models. Several illustrative examples are included, covering constant and time-varying models for both univariate and multivariate time series. Maximum likelihood and Bayesian methods to obtain parameter estimates are considered.

Suggested Citation

  • Petris, Giovanni & Petrone, Sonia, 2011. "State Space Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 41(i04).
    • Handle: RePEc:jss:jstsof:v:041:i04
    DOI: http://hdl.handle.net/10.18637/jss.v041.i04
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    References listed on IDEAS

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    1. Håvard Rue & Sara Martino & Nicolas Chopin, 2009. "Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(2), pages 319-392, April.
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    Cited by:

    1. Helske, Jouni, 2017. "KFAS: Exponential Family State Space Models in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 78(i10).
    2. Jong-Min Kim & Bainwen Sun & Sunghae Jun, 2019. "Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    3. Akın, Melda, 2015. "A novel approach to model selection in tourism demand modeling," Tourism Management, Elsevier, vol. 48(C), pages 64-72.
    4. Schütz, Peter & Westgaard, Sjur, 2018. "Optimal hedging strategies for salmon producers," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 60-70.
    5. Gómez, Victor, 2015. "SSMMATLAB: A Set of MATLAB Programs for the Statistical Analysis of State Space Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 66(i09).
    6. Lammerding, Marc & Stephan, Patrick & Trede, Mark & Wilfling, Bernd, 2013. "Speculative bubbles in recent oil price dynamics: Evidence from a Bayesian Markov-switching state-space approach," Energy Economics, Elsevier, vol. 36(C), pages 491-502.
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    8. Batool, Aamina & Kartal, Veysi & Ali, Zulfiqar & Scholz, Miklas & Ali, Farman, 2025. "A novel regional forecastable multiscalar standardized drought index (RFMSDI) for regional drought monitoring and assessment," Agricultural Water Management, Elsevier, vol. 308(C).
    9. Strickland, Christopher & Burdett, Robert & Mengersen, Kerrie & Denham, Robert, 2014. "PySSM: A Python Module for Bayesian Inference of Linear Gaussian State Space Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(i06).
    10. Allin Cottrell & Riccardo (Jack) Lucchetti & Matteo Pelagatti, 2016. "Measures of variance for smoothed disturbances in linear state-space models: a clarification," gretl working papers 3, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
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    12. Cristiana Tudor, 2016. "Predicting the Evolution of CO 2 Emissions in Bahrain with Automated Forecasting Methods," Sustainability, MDPI, vol. 8(9), pages 1-10, September.

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