predict {BGVAR} | R Documentation |

## Predictions

### Description

A function that computes predictions and conditional predictions based on a object of class `bgvar`

.

### Usage

```
## 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 |

`...` |
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 |

`constr_sd` |
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 |

### 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.

### Value

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.

### Author(s)

Maximilian Boeck, Martin Feldkircher, Florian Huber

### 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.

### 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)
```

*BGVAR*version 2.5.7 Index]