predict.tsmarch: Model Prediction

In tsmarch: Multivariate ARCH Models

Model Prediction

Description

Prediction function for estimated objects.

Usage

## 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,
  ...
)

Arguments

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.

Details

For the Copula GARCH model, the prediction is based on simulation due to the nonlinear transformation present in the model.

Value

A prediction class object for which methods exists for extracting relevant statistics such as the correlation, covariance, etc.

tsmarch documentation built on April 3, 2025, 7:40 p.m.