coef.tegarch {betategarch}

Extraction methods for 'tegarch' objects

Description

Extraction methods for objects of class 'tegarch' (i.e. the result of estimating a Beta-Skew-t-EGARCH model)

Usage

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

Arguments

object

an object of class 'tegarch'

x

an object of class 'tegarch'

verbose

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

standardised

logical. If TRUE (default) then the standardised residuals are returned. If FALSE then the scaled (by sigma) residuals are returned

...

additional arguments

Details

Empty

Value

coef:

A numeric vector containing the parameter estimates

fitted:

A zoo object. If verbose=FALSE (default), then the zoo object is a vector containing the fitted conditional standard deviations. If verbose = TRUE, then the zoo object is a matrix containing the return series y, fitted scale (sigma), fitted conditional standard deviation (stdev), fitted log-scale (lambda), dynamic component(s) (lambdadagger in the 1-component specification, lambda1dagger and lambda2dagger in the 2-compoment specification), the score (u), scaled residuals (epsilon) and standardised residuals (residstd)

logLik:

The value of the log-likelihood at the maximum

print:

Prints the most important parts of the estimation results

residuals:

A zoo object. If standardised = TRUE (default), then the zoo object is a vector with the standardised residuals. If standardised = FALSE, then the zoo vector contains the scaled residuals

summary:

A list. If verbose = FALSE, then only the most important entries are returned. If verbose = TRUE, then all entries apart from the 1st. (the y series) is returned

vcov:

The variance-covariance matrix of the estimated coefficents. The matrix is obtained by inverting the numerically estimated Hessian

Author(s)

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

References

Fernandez and Steel (1998), 'On Bayesian Modeling of Fat Tails and Skewness', Journal of the American Statistical Association 93, pp. 359-371.

Harvey and Sucarrat (2014), 'EGARCH models with fat tails, skewness and leverage'. Computational Statistics and Data Analysis 76, pp. 320-338.

Sucarrat (2013), 'betategarch: Simulation, Estimation and Forecasting of First-Order Beta-Skew-t-EGARCH models'. The R Journal (Volume 5/2), pp. 137-147.

See Also

tegarch, coef, fitted, logLik, predict, predict.tegarch, print, summary, vcov

Examples

#simulate 500 observations from model with default parameter values:
set.seed(123)
y <- tegarchSim(500)

#estimate and store as 'mymodel':
mymod <- tegarch(y)

#print estimation result:
print(mymod)

#extract coefficients:
coef(mymod)

#extract log-likelihood:
logLik(mymod)

#plot fitted conditional standard deviations:
plot(fitted(mymod))

#plot all the fitted series:
plot(fitted(mymod, verbose=TRUE))

#histogram of standardised residuals:
hist(residuals(mymod))