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Joint maximum likelihood estimation as implemented by fGarch::sgedFit.

Usage

mlsged(x, na.rm = FALSE, ...)

Arguments

x

a (non-empty) numeric vector of data values.

na.rm

logical. Should missing values be removed?

...

currently affects nothing.

Value

mlsged returns an object of class

univariateML. This is a named numeric vector with maximum likelihood estimates for the parameters mean, sd, nu, xi, and the following attributes:

model

The name of the model.

density

The density associated with the estimates.

logLik

The loglikelihood at the maximum.

support

The support of the density.

n

The number of observations.

call

The call as captured my match.call

Details

For the density function of the Student t-distribution see sged.

References

Nelson D.B. (1991); Conditional Heteroscedasticity in Asset Returns: A New Approach, Econometrica, 59, 347<U+2013>370.

Fernandez C., Steel M.F.J. (2000); On Bayesian Modelling of Fat Tails and Skewness, Preprint.

See also

sged for the Student t-density.

Examples

mlsged(precip)
#> Maximum likelihood estimates for the Skew Generalized Error model 
#>    mean       sd       nu       xi  
#> 34.9279  13.8051   1.4104   0.7737