Estimation of mixed-frequency Bayesian vector autoregressive (VAR) models with Minnesota or steady-state priors. The package implements a state space-based VAR model that handles mixed frequencies of the data. The model is estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution, where the prior can be either the Minnesota prior, as used by Schorfheide and Song (2015) <doi:10.1080/07350015.2014.954707>, or the steady-state prior, as advocated by Ankargren, Unosson and Yang (2018) <http://uu.diva-portal.org/smash/get/diva2:1260262/FULLTEXT01.pdf>.
|Author||Sebastian Ankargren [cre, aut] (<https://orcid.org/0000-0003-4415-8734>), Yukai Yang [aut]|
|Maintainer||Sebastian Ankargren <[email protected]>|
|Package repository||View on CRAN|
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