A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R
Tekijät: Helske Jouni
Julkaisuvuosi: 2022
Journal: SoftwareX
Tietokannassa oleva lehden nimi: SoftwareX
Vuosikerta: 18
DOI: https://doi.org/10.1016/j.softx.2022.101016
Tiivistelmä
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.
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.
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