Predictions
A function that computes predictions and conditional predictions based on a object of class bgvar.
## S3 method for class 'bgvar' predict( object, ..., n.ahead = 1, constr = NULL, constr_sd = NULL, quantiles = NULL, save.store = FALSE, verbose = TRUE )
object |
an object of class |
... |
additional arguments. |
n.ahead |
the forecast horizon. |
constr |
a matrix containing the conditional forecasts of size horizon times K, where horizon corresponds to the forecast horizon specified in |
constr_sd |
a matrix containing the standard deviations around the conditional forecasts. Must have the same size as |
quantiles |
Numeric vector with posterior quantiles. Default is set to compute median along with 68%/80%/90% confidence intervals. |
save.store |
If set to |
verbose |
If set to |
Predictions are performed up to an horizon of n.ahead. Note that conditional forecasts need a fully identified system. Therefore this function utilizes short-run restrictions via the Cholesky decomposition on the global solution of the variance-covariance matrix of the Bayesian GVAR.
Returns an object of class bgvar.pred with the following elements
fcast is a K times n.ahead times Q-dimensional array that contains Q quantiles of the posterior predictive distribution.
xglobal is a matrix object of dimension T times N (T # of observations, K # of variables in the system).
n.ahead specified forecast horizon.
lps.stats is an array object of dimension K times 2 times n.ahead and contains the mean and standard deviation of the log-predictive scores for each variable and each forecast horizon.
hold.out if h is not set to zero, this contains the hold-out sample.
Maximilian Boeck, Martin Feldkircher, Florian Huber
Jarocinski, M. (2010) Conditional forecasts and uncertainty about forecasts revisions in vector autoregressions. Economics Letters, Vol. 108(3), pp. 257-259.
Waggoner, D., F. and T. Zha (1999) Conditional Forecasts in Dynamic Multivariate Models. Review of Economics and Statistics, Vol. 81(4), pp. 639-561.
library(BGVAR)
data(eerDatasmall)
model.ssvs <- bgvar(Data=eerDatasmall,W=W.trade0012.small,plag=1,draws=100,burnin=100,
prior="SSVS")
fcast <- predict(model.ssvs, n.ahead=8)
# conditional predictions
# et up constraints matrix of dimension n.ahead times K
constr <- matrix(NA,nrow=8,ncol=ncol(model.ssvs$xglobal))
colnames(constr) <- colnames(model.ssvs$xglobal)
constr[1:5,"US.Dp"] <- model.ssvs$xglobal[76,"US.Dp"]
# add uncertainty to conditional forecasts
constr_sd <- matrix(NA,nrow=8,ncol=ncol(model.ssvs$xglobal))
colnames(constr_sd) <- colnames(model.ssvs$xglobal)
constr_sd[1:5,"US.Dp"] <- 0.001
fcast_cond <- predict(model.ssvs, n.ahead=8, constr=constr, constr_sd=constr_sd)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.