Functions and tools for estimation of mixed-frequency Bayesian vector autoregressive (VAR) models. The package implements a state space-based VAR model that handles mixed frequencies of the data as proposed by Schorfheide and Song (2015) <doi:10.1080/07350015.2014.954707>, and extensions thereof developed by Ankargren, Unosson and Yang (2020) <doi:10.1515/jtse-2018-0034>, Ankargren and Joneus (2019) <arXiv:1912.02231>, and Ankargren and Joneus (2020) <doi:10.1016/j.ecosta.2020.05.007>. The models are estimated using Markov Chain Monte Carlo to numerically approximate the posterior distribution. Prior distributions that can be used include normal-inverse Wishart and normal-diffuse priors as well as steady-state priors. Stochastic volatility can be handled by common or factor stochastic volatility models.
|Author||Sebastian Ankargren [cre, aut] (<https://orcid.org/0000-0003-4415-8734>), Yukai Yang [aut] (<https://orcid.org/0000-0002-2623-8549>), Gregor Kastner [ctb] (<https://orcid.org/0000-0002-8237-8271>)|
|Maintainer||Sebastian Ankargren <email@example.com>|
|Package repository||View on CRAN|
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