predict function

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
)

Arguments

• object: An object of class bgvar.
• ...: Additional arguments.
• n.ahead: Forecast horizon.
• constr: Matrix containing the conditional forecasts of size horizon times K, where horizon corresponds to the forecast horizon specified in pred.obj, while K is the number of variables in the system. The ordering of the variables have to correspond the ordering of the variables in the system. Rest is just set to NA.
• constr_sd: Matrix containing the standard deviations around the conditional forecasts. Must have the same size as constr.
• quantiles: Numeric vector with posterior quantiles. Default is set to compute median along with 68%/80%/90% confidence intervals.
• save.store: If set to TRUE the full distribution is returned. Default is set to FALSE in order to save storage.
• verbose: If set to FALSE it suppresses printing messages to the console.

Returns

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.

Details

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.

Examples

library(BGVAR)
data(testdata)
model.ssvs <- bgvar(Data=testdata,W=W.test,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)

References

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.

Author(s)

Maximilian Boeck, Martin Feldkircher, Florian Huber