coef.garchx: Extraction functions for 'garchx' objects

In garchx: Flexible and Robust GARCH-X Modelling

Extraction functions for 'garchx' objects

Description

Extraction functions for objects of class 'garchx'

Usage

  ## S3 method for class 'garchx'
coef(object, ...)
  ## S3 method for class 'garchx'
fitted(object, as.zoo = TRUE, ...)
  ## S3 method for class 'garchx'
logLik(object, ...)
  ## S3 method for class 'garchx'
nobs(object, ...)
  ## S3 method for class 'garchx'
predict(object, n.ahead = 10, newxreg = NULL,
    newindex = NULL, n.sim = NULL, verbose = FALSE, ...)
  ## S3 method for class 'garchx'
print(x, ...)
  ## S3 method for class 'garchx'
quantile(x, probs=0.025, names = TRUE, type = 7, as.zoo = TRUE, ...)
  ## S3 method for class 'garchx'
residuals(object, as.zoo = TRUE, ...)
  ## S3 method for class 'garchx'
toLatex(object, digits = 4, ...)
  ## S3 method for class 'garchx'
vcov(object, vcov.type = NULL, ...)

Arguments

object	an object of class 'garchx'
x	an object of class 'garchx'
as.zoo	logical. If TRUE, then the returned result is of class zoo
n.ahead	integer that determines how many steps ahead predictions should be generated
newxreg	vector or matrix with the out-of-sample regressor values
newindex	zoo-index for the out-of-sample predictions. If NULL (default), then 1:n.ahead is used
n.sim	NULL or an integer, the number of simulations
verbose	logical. If TRUE, then the simulations - in addition to the predictions - are returned
probs	vector of probabilities
names	logical, whether to return names or not
type	integer that determines the algorithm used to compute the quantile, see quantile
digits	integer, the number of digits in the printed LaTeX code
vcov.type	NULL or a character that is (partially) matched to "ordinary" or "robust". The robust coefficient-covariance is that of Francq and Thieu (2018).
...	additional arguments

Value

coef:	numeric vector containing parameter estimates
fitted:	fitted conditional variance
logLik:	log-likelihood (normal density)
nobs:	the number of observations used in the estimation
predict:	a vector with the predictions (verbose=FALSE), or a matrix with both the predictions and the simulations (verbose=TRUE)
print:	print of the estimation results
quantile:	the fitted quantiles, i.e. the conditional standard deviation times the empirical quantile of the standardised innovations
residuals:	standardised residuals
vcov:	coefficient variance-covariance matrix

Author(s)

Genaro Sucarrat, http://www.sucarrat.net/

References

Christian Francq and Le Quien Thieu (2018): 'QML inference for volatility models with covariates', Econometric Theory, doi:10.1017/S0266466617000512

See Also

garchx, garchxSim, zoo

Examples

##simulate from a garch(1,1):
set.seed(123)
y <- garchxSim(1000)

##estimate garch(1,1) model:
mymod <- garchx(y)

##print estimation results:
print(mymod)

##extract coefficients:
coef(mymod)

##extract and store conditional variances:
sigma2hat <- fitted(mymod)

##extract log-likelihood:
logLik(mymod)

##extract and store standardised residuals:
etahat <- residuals(mymod)

##extract coefficient variance-covariance matrix:
vcov(mymod)

##generate predictions:
predict(mymod)

garchx documentation built on Sept. 13, 2022, 5:06 p.m.