summary.skewhypFit: Summarising the Skew Hyperbolic Student t-Distribution Fit

Description

summary Method for class "skewhypFit".

Usage

# S3 method for skewhypFit
summary(object, ...)
# S3 method for summary.skewhypFit
print(x, digits = max(3,
getOption("digits") - 3), ...)

Value

If the Hessian is available summary.skewhyhpFit computes standard errors of $\mu$, $\delta$, $\beta$

and $\nu$, and adds them to object as

object$sds. Otherwise, no calculations are performed and the composition object is unaltered.

summary.skewhypFit invisibly returns x with class changed to summary.skewhypFit.

See skewhypFit for the composition of an object of class

skewhypFit.

print.summary.skewhypFit prints a summary in the same format as

print.skewhypFit when the Hessian is not available from the fit. When the Hessian is available, the standard errors for the parameter estimates are printed in parentheses beneath the parameter estimates, in the manner of fitdistr in the package MASS.

Arguments

object: An object of class "skewhypFit", resulting from a call to skewhypFit.
x: An object of class "summary.skewhypFit", resulting from a call to
summary.skewhypFit.
digits: The number of significant digits to use when printing.
...: Further arguments passed to or from other methods.

Author

David Scott d.scott@auckland.ac.nz, Fiona Grimson

Details

summary.skewhypFit calculates standard errors for errors for the estimates of $\mu$, $\delta$, $\beta$ and $\nu$ of the skew hyperbolic Student t-distribution parameter vector param, if the Hessian from the call to optim or nlm is available. Because the parameters in the call to the optimiser are $\mu$, $\log(\delta)$, $\beta$ and $\log(\nu)$ the delta method is used to obtain standard errors for $\delta$ and $\nu$

References

Aas, K. and Haff, I. H. (2006). The Generalised Hyperbolic Skew Student's t-distribution, Journal of Financial Econometrics, 4, 275--309.

Examples

Run this code

## Continuing the skewhypFit(.) example:
data(lrdji)
djfit <- skewhypFit(lrdji, print = FALSE, plot = FALSE, hessian = TRUE)
print(djfit)
summary(djfit)

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