bvar {BVAR} | R Documentation |
Hierarchical Bayesian vector autoregression
Description
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
.
Usage
bvar(
data,
lags,
n_draw = 10000L,
n_burn = 5000L,
n_thin = 1L,
priors = bv_priors(),
mh = bv_mh(),
fcast = NULL,
irf = NULL,
verbose = TRUE,
...
)
Arguments
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. |
Details
The model can be expressed as:
y_t = a_0 + A_1 y_{t-1} + ... + A_p y_{t-p} + \epsilon_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:
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.
Value
Returns a list of class bvar
with the following elements:
-
beta
- Numeric array with draws from the posterior of the VAR coefficients. Also seecoef.bvar
. -
sigma
- Numeric array with draws from the posterior of the variance-covariance matrix. Also seevcov.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 ofoptim
, which is used to find starting values for the hyperparameters. -
prior
- Prior settings frombv_priors
. -
call
- Call to the function. Seematch.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 frompredict.bvar
. -
irf
- Impulse responses fromirf.bvar
.
Author(s)
Nikolas Kuschnig, Lukas Vashold
References
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
;
Examples
# 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)