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An R Package for Dynamic Linear Models

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  • Petris, Giovanni

Abstract

We describe an R package focused on Bayesian analysis of dynamic linear models. The main features of the package are its flexibility to deal with a variety of constant or time-varying, univariate or multivariate models, and the numerically stable singular value decomposition-based algorithms used for filtering and smoothing. In addition to the examples of "out-of-the-box" use, we illustrate how the package can be used in advanced applications to implement a Gibbs sampler for a user-specified model.

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  • Petris, Giovanni, 2010. "An R Package for Dynamic Linear Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i12).
    • Handle: RePEc:jss:jstsof:v:036:i12
    DOI: http://hdl.handle.net/10.18637/jss.v036.i12
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    Cited by:

    1. Peter Knaus & Angela Bitto-Nemling & Annalisa Cadonna & Sylvia Fruhwirth-Schnatter, 2019. "Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP," Papers 1907.07065, arXiv.org, revised Nov 2020.
    2. Kleyton da Costa & Felipe Leite Coelho da Silva & Josiane da Silva Cordeiro Coelho & Andr'e de Melo Modenesi, 2020. "A Systematic Comparison of Forecasting for Gross Domestic Product in an Emergent Economy," Papers 2010.13259, arXiv.org, revised Mar 2022.
    3. Cabral, Celso Rômulo Barbosa & da-Silva, Cibele Queiroz & Migon, Helio S., 2014. "A dynamic linear model with extended skew-normal for the initial distribution of the state parameter," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 64-80.
    4. Shao, Wei & Guo, Guangbao & Meng, Fanyu & Jia, Shuqin, 2013. "An efficient proposal distribution for Metropolis–Hastings using a B-splines technique," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 465-478.
    5. Ruiz-Cárdenas, Ramiro & Krainski, Elias T. & Rue, Håvard, 2012. "Direct fitting of dynamic models using integrated nested Laplace approximations — INLA," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1808-1828.
    6. Sun, He & Cabras, Stefano, 2023. "Deep Learning and Bayesian Calibration Approach to Hourly Passenger Occupancy Prediction in Beijing Metro: A Study Exploiting Cellular Data and Metro Conditions," DES - Working Papers. Statistics and Econometrics. WS 38783, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Reusens Peter & Croux Christophe, 2017. "Detecting time variation in the price puzzle: a less informative prior choice for time varying parameter VAR models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(4), pages 1-18, September.
    8. Andrés Ramírez Hassan & Javier Pantoja Robayo, 2013. "Co-movements between Latin American and U.S. stock markets: convergence after the financial crisis," Documentos de Trabajo de Valor Público 10931, Universidad EAFIT.
    9. Schütz, Peter & Westgaard, Sjur, 2018. "Optimal hedging strategies for salmon producers," Journal of Commodity Markets, Elsevier, vol. 12(C), pages 60-70.
    10. Ekici, Oya & Nemlioğlu, Karun, 2017. "Emerging economies’ short-term private external debt as evidence of economic crisis," Journal of Policy Modeling, Elsevier, vol. 39(2), pages 232-246.

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