bvar {BVAR} | R Documentation |
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
.
bvar( data, lags, n_draw = 10000L, n_burn = 5000L, n_thin = 1L, priors = bv_priors(), mh = bv_mh(), fcast = NULL, irf = NULL, verbose = TRUE, ... )
data |
Numeric matrix or dataframe. Note that observations are expected to be ordered from earliest to latest, and variables in the columns. |
lags |
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. |
n_thin |
Integer scalar. Every n_thin'th iteration is stored. For a given memory requirement thinning reduces autocorrelation, while increasing effective sample size. |
priors |
Object from |
mh |
Object from |
fcast |
Object from |
irf |
Object from |
verbose |
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 (2019) and Giannone, Lenza and Primiceri (2015)
for further information.
Methods for a bvar
object and its derivatives can be used to:
predict and analyse scenarios;
evaluate shocks and the variance of forecast errors;
visualise forecasts and impulse responses, parameters and residuals;
retrieve coefficents and the variance-covariance matrix;
calculate fitted and residual values;
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:
beta
- Numeric array with draws from the posterior of the VAR
coefficients. Also see coef.bvar
.
sigma
- Numeric array with draws from the posterior of the
variance-covariance matrix. Also see vcov.bvar
.
hyper
- Numeric matrix with draws from the posterior of the
hierarchically treated hyperparameters.
ml
- Numeric vector with the marginal likelihood (with respect
to the hyperparameters), that determines acceptance probability.
optim
- List with outputs of optim
,
which is used to find starting values for the hyperparameters.
prior
- Prior settings from bv_priors
.
call
- Call to the function. See match.call
.
meta
- List with meta information. Includes the number of
variables, accepted draws, number of iterations, and data.
variables
- Character vector with the column names of
data. If missing, variables are named iteratively.
explanatories
- Character vector with names of explanatory
variables. Formatting is akin to: "FEDFUNDS-lag1"
.
fcast
- Forecasts from predict.bvar
.
irf
- Impulse responses from irf.bvar
.
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, https://doi.org/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, forthcoming.
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(x) # Plot forecasts and impulse responses plot(predict(x)) plot(irf(x)) # Check coefficient values and variance-covariance matrix summary(x) ## End(Not run)