Generalized Error distribution maximum likelihood estimation

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

Usage

mlged(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

mlged returns an object of class

univariateML. This is a named numeric vector with maximum likelihood estimates for the parameters mean, sd, nu 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 ged.

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

ged for the Student t-density.

Examples

mlged(precip)
#> Maximum likelihood estimates for the Generalized Error model 
#>   mean      sd      nu  
#> 35.330  13.626   1.772