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.


Reference manual

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0.5.6 by Sebastian Ankargren, 10 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  

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