predict.cgarch.estimate {tsmarch} | R Documentation |
Prediction function for estimated objects.
## S3 method for class 'cgarch.estimate'
predict(
object,
h = 1,
nsim = 1000,
sim_method = c("parametric", "bootstrap"),
forc_dates = NULL,
cond_mean = NULL,
seed = NULL,
...
)
## S3 method for class 'dcc.estimate'
predict(
object,
h = 1,
nsim = 1000,
sim_method = c("parametric", "bootstrap"),
forc_dates = NULL,
cond_mean = NULL,
seed = NULL,
...
)
## S3 method for class 'gogarch.estimate'
predict(
object,
h = 1,
nsim = 1000,
sim_method = c("parametric", "bootstrap"),
forc_dates = NULL,
cond_mean = NULL,
seed = NULL,
...
)
object |
an estimated object from one of the models in the package. |
h |
the forecast horizon. |
nsim |
the number of sample paths to generate. |
sim_method |
white noise method for generating random sample for the multivariate distribution. The default “parametric” samples random normal variates whilst the “bootstrap” samples from the whitened innovations of the fitted model. |
forc_dates |
an optional vector of forecast dates equal to h. If NULL will use the implied periodicity of the data to generate a regular sequence of dates after the last available date in the data. |
cond_mean |
an optional matrix (h x n_series) of the predicted conditional mean for the series which is used to recenter the simulated predictive distribution. |
seed |
an integer that will be used in a call to set.seed before simulating. |
... |
no additional arguments currently supported. |
For the Copula GARCH model, the prediction is based on simulation due to the nonlinear transformation present in the model.
A prediction class object for which methods exists for extracting relevant statistics such as the correlation, covariance, etc.