forecast: Forecasting with VAR, DSGE, and DSGE-VAR Models.

In kthohr/BMR: Bayesian Macroeconometrics in R

Description

Compute forecasts using VAR, DSGE, and DSGE-VAR models.

Usage

forecast(obj,...)

## S3 method for class 'Rcpp_bvarm'
forecast(obj,periods=20,shocks=TRUE,plot=TRUE,
         var_names=NULL,percentiles=c(.05,.50,.95),use_mean=FALSE,
         back_data=0,save=FALSE,height=13,width=11,...)
## S3 method for class 'Rcpp_bvars'
forecast(obj,periods=20,shocks=TRUE,plot=TRUE,
         var_names=NULL,percentiles=c(.05,.50,.95),use_mean=FALSE,
         back_data=0,save=FALSE,height=13,width=11,...)
## S3 method for class 'Rcpp_bvarw'
forecast(obj,periods=20,shocks=TRUE,plot=TRUE,
         var_names=NULL,percentiles=c(.05,.50,.95),use_mean=FALSE,
         back_data=0,save=FALSE,height=13,width=11,...)
## S3 method for class 'Rcpp_cvar'
forecast(obj,periods=20,shocks=TRUE,plot=TRUE,
         var_names=NULL,percentiles=c(.05,.50,.95),use_mean=FALSE,
         back_data=0,save=FALSE,height=13,width=11,...)

## S3 method for class 'Rcpp_dsge_gensys'
forecast(obj,periods=20,shocks=TRUE,plot=TRUE,
         var_names=NULL,percentiles=c(.05,.50,.95),use_mean=FALSE,
         back_data=0,save=FALSE,height=13,width=11,...)
## S3 method for class 'Rcpp_dsge_uhlig'
forecast(obj,periods=20,shocks=TRUE,plot=TRUE,
         var_names=NULL,percentiles=c(.05,.50,.95),use_mean=FALSE,
         back_data=0,save=FALSE,height=13,width=11,...)

## S3 method for class 'Rcpp_dsgevar_gensys'
forecast(obj,periods=20,shocks=TRUE,plot=TRUE,
         var_names=NULL,percentiles=c(.05,.50,.95),use_mean=FALSE,
         back_data=0,save=FALSE,height=13,width=11,...)
## S3 method for class 'Rcpp_dsgevar_uhlig'
forecast(obj,periods=20,shocks=TRUE,plot=TRUE,
         var_names=NULL,percentiles=c(.05,.50,.95),use_mean=FALSE,
         back_data=0,save=FALSE,height=13,width=11,...)

Arguments

obj	An object of one of the above classes.
periods	The forecast horizon.
shocks	Whether to include uncertainty about future shocks when calculating the forecasts.
plot	Whether to plot the forecasts.
var_names	Variable names.
percentiles	The percentiles of the conditional posterior distribution of forecasts to use for plotting.
use_mean	Whether to use the mean of the forecast distribution rather than the middle value in ‘percentiles’.
back_data	How many 'real' data points to plot before plotting the forecast. A broken line will indicate whether the ‘real’ data ends and the forecast begins.
save	Whether to save the plots.
height	If save=FALSE, use this to set the height of the plot.
width	If save=FALSE, use this to set the width of the plot.

...	Additional arguments (not used).

Value

The function returns a plot of the forecast with user-selected percentiles, as well as the values used to create the plot; see the vignette for more details on the values returned.

Author(s)

Keith O'Hara

Examples

## Not run: 
data(BMRVARData)
bvar_data <- data.matrix(USMacroData[,2:4])

#

coef_prior <- c(0.9,0.9,0.9)
XiBeta <- 4
XiSigma <- 1
gamma = 4

bvar_obj <- new(bvarw)

#

bvar_obj$build(bvar_data,TRUE,4)
bvar_obj$prior(coef_prior,XiBeta,XiSigma,gamma)
bvar_obj$gibbs(10000,5000)

forecast(bvar_obj,shocks=TRUE,var_names=colnames(bvar_data),back_data=10,save=FALSE)

## End(Not run)

kthohr/BMR documentation built on May 20, 2019, 7:04 p.m.