mfbvar: Mixed-Frequency Bayesian VAR Models

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. The model is 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.

Version: 0.5.1
Depends: R (≥ 3.0.2)
Imports: Rcpp (≥ 0.12.7), ggplot2 (≥ 2.2.1), methods, lubridate, GIGrvg, stochvol (≥ 2.0.3), RcppParallel, dplyr, magrittr, tibble
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, stochvol (≥ 2.0.3), RcppParallel
Suggests: testthat, covr, knitr, ggridges, alfred, tidyverse, factorstochvol
Published: 2019-09-02
Author: Sebastian Ankargren ORCID iD [cre, aut], Yukai Yang ORCID iD [aut]
Maintainer: Sebastian Ankargren <sebastian.ankargren at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: README NEWS
CRAN checks: mfbvar results


Reference manual: mfbvar.pdf
Vignettes: Bayesian Mixed-Frequency VARs
Package source: mfbvar_0.5.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X binaries: r-release: mfbvar_0.5.1.tgz, r-oldrel: mfbvar_0.5.1.tgz
Old sources: mfbvar archive


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