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 editorsHelske, Jouni

Journal or seriesSoftwareX

eISSN2352-7110

Publication year2022

Publication date24/02/2022

Volume18

Article number101016

PublisherElsevier BV

Publication countryNetherlands

Publication languageEnglish

DOIhttps://doi.org/10.1016/j.softx.2022.101016

Publication open accessOpenly available

Publication channel open accessOpen 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 informationOriginal 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.


Keywordstime seriesregression analysislinear modelsBayesian analysisMarkov chainsMonte Carlo methodsR (programming languages)

Free keywordsBayesian inference; time-varying regression; R; Markov chain Monte Carlo


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Ministry reportingYes

Reporting Year2022

JUFO rating1


Last updated on 2024-22-04 at 17:40