Toolkit for the estimation of hierarchical Bayesian vector autoregressions. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015). Allows for the computation of impulse responses and forecasts and provides functionality for assessing results.
Estimation of hierarchical Bayesian vector autoregressive models following Kuschnig & Vashold (2021). Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015). Functions to calculate forecasts, and compute and identify impulse responses and forecast error variance decompositions are available. Several methods to print, plot and summarise results facilitate analysis.
BVAR is available on CRAN. The development version can be installed from GitHub.
install.packages("BVAR")devtools::install_github("nk027/BVAR")
The main function to perform hierarchical Bayesian VAR estimation is bvar(). Calls can be customised with regard to the sampling (e.g. via n_draw, or see bv_mh()) or with regard to the priors (see bv_priors()). Forecasts and impulse responses can be computed at runtime, or afterwards (see predict() and irf()). Identification of sign restrictions can be achieved recursively, via sign restrictions, or via zero and sign restrictions.
Analysis is facilitated by a variety of standard methods. The default plot() method provides trace and density plots of hyperparameters and optionally coefficients. Impulse responses and forecasts can easily be assessed with the provided plot() methods. Other available methods include summary(), fitted(), residuals(), coef(), vcov() and density(). Note that BVAR generates draws from the posterior — all methods include functionality to access this distributional information. Information can be obtained directly or more conveniently using the BVARverse package.
BVAR comes with the FRED-MD and FRED-QD datasets (McCracken and Ng, 2016). They can be accessed using data("fred_md") or data("fred_qd") respectively. The dataset is licensed under a modified ODC-BY 1.0 license, that is available in the provided LICENSE file.
# Load the packagelibrary("BVAR")# Access a subset of the fred_qd datasetdata <- fred_qd[, c("GDPC1", "CPIAUCSL", "UNRATE", "FEDFUNDS")]# Transform it to be stationarydata <- fred_transform(data, codes = c(5, 5, 5, 1), lag = 4)# Estimate using default priors and MH stepx <- bvar(data, lags = 1)# Check convergence via trace and density plotsplot(x)# Calculate and store forecasts and impulse responsespredict(x) <- predict(x, horizon = 20)irf(x) <- irf(x, horizon = 20, identification = TRUE)# Plot forecasts and impulse responsesplot(predict(x))plot(irf(x))
Nikolas Kuschnig and Lukas Vashold (2021). BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R. Journal of Statistical Software, 14, 1-27, DOI: 10.18637/jss.v100.i14.
Domenico Giannone, Michele Lenza and Giorgio E. Primiceri (2015). Prior Selection for Vector Autoregressions. The Review of Economics and Statistics, 97:2, 436-451, DOI: 10.1162/REST_a_00483.
Michael W. McCracken and Serena Ng (2016). FRED-MD: A Monthly Database for Macroeconomic Research. Journal of Business & Economic Statistics, 34:4, 574-589, DOI: 10.1080/07350015.2015.1086655.