coef.garchx {garchx}

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)