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.


Reference manual

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0.5.4 by Sebastian Ankargren, 2 months ago


Report a bug at https://github.com/ankargren/mfbvar/issues

Browse source code at https://github.com/cran/mfbvar

Authors: Sebastian Ankargren [cre, aut] , Yukai Yang [aut] , Gregor Kastner [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, ggplot2, methods, lubridate, GIGrvg, stochvol, RcppParallel, dplyr, magrittr, tibble, zoo

Suggests testthat, covr, knitr, ggridges, alfred, factorstochvol

Linking to Rcpp, RcppArmadillo, RcppProgress, stochvol, RcppParallel

System requirements: GNU make

See at CRAN