predict.MSGARCH_SPEC | R Documentation |
Conditional volatility (and predictive distribution) forecasts.
## S3 method for class 'MSGARCH_SPEC' predict( object, newdata = NULL, nahead = 1L, do.return.draw = FALSE, par = NULL, do.cumulative = FALSE, ctr = list(), ... ) ## S3 method for class 'MSGARCH_ML_FIT' predict( object, newdata = NULL, nahead = 1L, do.return.draw = FALSE, do.cumulative = FALSE, ctr = list(), ... ) ## S3 method for class 'MSGARCH_MCMC_FIT' predict( object, newdata = NULL, nahead = 1L, do.return.draw = FALSE, do.cumulative = FALSE, ctr = list(), ... )
object |
Model specification of class |
newdata |
Vector (of size T*) of new observations. (Default |
nahead |
Scalar indicating the number of step-ahead evaluation. |
do.return.draw |
Are simulation draws from the predictive distribution
returned? (Default |
par |
Vector (of size d) or matrix (of size |
do.cumulative |
Logical indicating if the conditional volatility
prediction is computed on the cumulative simulations (typically log-returns, as they can be aggregated).
(Default: |
ctr |
A list of control parameters:
|
... |
Not used. Other arguments to |
If a matrix of MCMC posterior draws is given, the Bayesian predictive conditional volatility (and predictive distribution) forecasts are returned.
A list of class MSGARCH_FORECAST
with the following elements:
vol
: Condititional volatility forecast (vector of size nahead
).
draw
: If do.return.draw = TRUE
:
Draws sampled from the predictive distributions (matrix of size nahead
x nsim
).
If do.return.draw = FALSE
:
NULL
The MSGARCH_FORECAST
class contains the plot
method.
# create specification spec <- CreateSpec() # load data data("SMI", package = "MSGARCH") # predict from specification par <- c(0.1, 0.1, 0.8, 0.2, 0.1, 0.8, 0.99, 0.01) set.seed(1234) pred <- predict(object = spec, par = par, newdata = SMI, nahead = 5L) head(pred) plot(pred) # predict from ML fit fit <- FitML(spec = spec, data = SMI) set.seed(1234) pred <- predict(object = fit, nahead = 5L, do.return.draw = TRUE) head(pred) plot(pred) ## Not run: set.seed(1234) fit <- FitMCMC(spec = spec, data = SMI) pred <- predict(object = fit, nahead = 5L, do.return.draw = TRUE) plot(pred) ## End(Not run)
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