bvar {BVAR}R Documentation

Hierarchical Bayesian vector autoregression


Used to estimate hierarchical Bayesian Vector Autoregression (VAR) models in the fashion of Giannone, Lenza and Primiceri (2015). Priors are adjusted and added via bv_priors. The Metropolis-Hastings step can be modified with bv_mh.


  n_draw = 10000L,
  n_burn = 5000L,
  n_thin = 1L,
  priors = bv_priors(),
  mh = bv_mh(),
  fcast = NULL,
  irf = NULL,
  verbose = TRUE,



Numeric matrix or dataframe. Note that observations are expected to be ordered from earliest to latest, and variables in the columns.


Integer scalar. Lag order of the model.

n_draw, n_burn

Integer scalar. The number of iterations to (a) cycle through and (b) burn at the start.


Integer scalar. Every n_thin'th iteration is stored. For a given memory requirement thinning reduces autocorrelation, while increasing effective sample size.


Object from bv_priors with prior settings. Used to adjust the Minnesota prior, add custom dummy priors, and choose hyperparameters for hierarchical estimation.


Object from bv_mh with settings for the Metropolis-Hastings step. Used to tune automatic adjustment of the acceptance rate within the burn-in period, or manually adjust the proposal variance.


Object from bv_fcast with forecast settings. Options include the horizon and settings for conditional forecasts i.e. scenario analysis. May also be calculated ex-post using predict.bvar.


Object from bv_irf with settings for the calculation of impulse responses and forecast error variance decompositions. Options include the horizon and different identification schemes. May also be calculated ex-post using irf.bvar.


Logical scalar. Whether to print intermediate results and progress.


Not used.


The model can be expressed as:

y_t = a_0 + A_1 y_{t-1} + ... + A_p y_{t-p} + e_t

See Kuschnig and Vashold (2021) and Giannone, Lenza and Primiceri (2015) for further information. Methods for a bvar object and its derivatives can be used to:

Note that these methods generally work by calculating quantiles from the posterior draws. The full posterior may be retrieved directly from the objects. The function str can be very helpful for this.


Returns a list of class bvar with the following elements:


Nikolas Kuschnig, Lukas Vashold


Giannone, D. and Lenza, M. and Primiceri, G. E. (2015) Prior Selection for Vector Autoregressions. The Review of Economics and Statistics, 97:2, 436-451, doi: 10.1162/REST_a_00483.

Kuschnig, N. and Vashold, L. (2021) BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R. Journal of Statistical Software, 14, 1-27, doi: 10.18637/jss.v100.i14.

See Also

bv_priors; bv_mh; bv_fcast; bv_irf; predict.bvar; irf.bvar; plot.bvar;


# Access a subset of the fred_qd dataset
data <- fred_qd[, c("CPIAUCSL", "UNRATE", "FEDFUNDS")]
# Transform it to be stationary
data <- fred_transform(data, codes = c(5, 5, 1), lag = 4)

# Estimate a BVAR using one lag, default settings and very few draws
x <- bvar(data, lags = 1, n_draw = 1000L, n_burn = 200L, verbose = FALSE)

# Calculate and store forecasts and impulse responses
predict(x) <- predict(x, horizon = 8)
irf(x) <- irf(x, horizon = 8, fevd = FALSE)

## Not run: 
# Check convergence of the hyperparameters with a trace and density plot
# Plot forecasts and impulse responses
# Check coefficient values and variance-covariance matrix

## End(Not run)

[Package BVAR version 1.0.2 Index]