A1 Journal article (refereed)
Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R (2022)
Helske, J. (2022). Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R. SoftwareX, 18, Article 101016. https://doi.org/10.1016/j.softx.2022.101016
JYU authors or editors
Publication details
All authors or editors: Helske, Jouni
Journal or series: SoftwareX
eISSN: 2352-7110
Publication year: 2022
Publication date: 24/02/2022
Volume: 18
Article number: 101016
Publisher: Elsevier BV
Publication country: Netherlands
Publication language: English
DOI: https://doi.org/10.1016/j.softx.2022.101016
Publication open access: Openly available
Publication channel open access: Open Access channel
Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/80507
Web address of parallel published publication (pre-print): https://arxiv.org/abs/2009.07063
Additional information: Original software publication.
Abstract
The R package walker extends standard Bayesian general linear models to the case where the effects of the explanatory variables can vary in time. This allows, for example, to model the effects of interventions such as changes in tax policy which gradually increases their effect over time. The Markov chain Monte Carlo algorithms powering the Bayesian inference are based on Hamiltonian Monte Carlo provided by Stan software, using a state space representation of the model to marginalize over the regression coefficients for efficient low-dimensional sampling.
Keywords: time series; regression analysis; linear models; Bayesian analysis; Markov chains; Monte Carlo methods; R (programming languages)
Free keywords: Bayesian inference; time-varying regression; R; Markov chain Monte Carlo
Contributing organizations
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Ministry reporting: Yes
VIRTA submission year: 2022
JUFO rating: 1