Mixed-Frequency Bayesian VAR Models

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) , and extensions thereof developed by Ankargren, Unosson and Yang (2020) , Ankargren and Joneus (2019) , and Ankargren and Joneus (2020) . 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.


News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("mfbvar")

0.5.6 by Sebastian Ankargren, 2 months ago


https://github.com/ankargren/mfbvar


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  


Task views: Time Series Analysis


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