Efficient Bayesian generalized linear models with time-varying coefficients : The walker package in R
Year of publication
2022
Authors
Helske, Jouni
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.
Show moreOrganizations and authors
University of Jyväskylä
Helske Jouni 
Publication type
Publication format
Article
Parent publication type
Journal
Article type
Original article
Audience
ScientificPeer-reviewed
Peer-ReviewedMINEDU's publication type classification code
A1 Journal article (refereed), original researchPublication channel information
Journal
Publisher
Volume
18
Article number
101016
ISSN
Publication forum
Publication forum level
1
Open access
Open access in the publisher’s service
Yes
Open access of publication channel
Fully open publication channel
Self-archived
Yes
Article processing fee (EUR)
220
Year of payment for the open publication fee
2022
Other information
Fields of science
Statistics and probability
Keywords
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Publication country
Netherlands
Internationality of the publisher
International
Language
English
International co-publication
No
Co-publication with a company
No
DOI
10.1016/j.softx.2022.101016
The publication is included in the Ministry of Education and Culture’s Publication data collection
Yes