coef.mlgarch {lgarch}

Extraction methods for 'mlgarch' objects

Description

Extraction methods for objects of class 'mlgarch' (i.e. the result of estimating a multivariate CCC-log-GARCH model)

Usage

## S3 method for class 'mlgarch'
coef(object, varma = FALSE, ...)
## S3 method for class 'mlgarch'
fitted(object, varma = FALSE, verbose = FALSE, ...)
## S3 method for class 'mlgarch'
logLik(object, varma = FALSE, ...)
## S3 method for class 'mlgarch'
print(x, varma = FALSE, ...)
## S3 method for class 'mlgarch'
residuals(object, varma = FALSE, ...)
## S3 method for class 'mlgarch'
summary(object, ...)
## S3 method for class 'mlgarch'
vcov(object, varma = FALSE, ...)

Arguments

object

an object of class 'mlgarch'

x

an object of class 'mlgarch'

verbose

logical. If FALSE (default), then only basic information is returned

varma

logical. If FALSE (default), then information relating to the multivariate CCC-log-GARCH model is returned. If TRUE, then information relating to the VARMA representation is returned

...

additional arguments

Details

Empty

Value

coef:

A numeric vector containing the parameter estimates

fitted:

A zoo object (a matrix). If verbose = FALSE (default), then the zoo object contains the fitted conditional standard deviations of each equation. If verbose = TRUE, then the zoo object also contains additional information

logLik:

The value of the log-likelihood (contributions at zeros excluded) at the maximum

print:

Prints the most important parts of the estimation results

residuals:

A zoo object (a matrix) with the residuals. If varma = FALSE (default), then the standardised residuals are returned. If varma = TRUE, then the residuals of the VARMA representation is returned

summary:

A print of the items in the mlgarch object

vcov:

The variance-covariance matrix

Author(s)

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

See Also

mlgarch

Examples

##simulate 1000 observations from 2-dimensional model w/default parameter values:
set.seed(123)
y <- mlgarchSim(1000)

##estimate a 2-dimensional ccc-log-garch(1,1):
mymod <- mlgarch(y)

##print results:
print(mymod)

##extract ccc-log-garch coefficients:
coef(mymod)

##extract Gaussian log-likelihood (zeros excluded) of the ccc-log-garch model:
logLik(mymod)

##extract Gaussian log-likelihood (zeros excluded) of the varma representation:
logLik(mymod, varma=TRUE)

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

##extract and plot the fitted conditional standard deviations:
sdhat <- fitted(mymod)
plot(sdhat)

##extract and plot standardised residuals:
zhat <- residuals(mymod)
plot(zhat)