Calculates an approximation to the Hessian of a function. Used for obtaining an approximation to the information matrix for maximum likelihood estimation.

1 |

`param` |
Numeric. The Hessian is to be evaluated at this point. |

`fun` |
A function of the parameters specified by |

`...` |
Values of other parameters of the function |

As a typical statistical application, the function `fun`

is the
log-likelihood function, `param`

specifies the maximum likelihood
estimates of the parameters of the distribution, and the data
constitutes the other parameter values required for determination of
the log-likelihood function.

The approximate Hessian matrix of the function `fun`

where
differentiation is with respect to the vector of parameters
`param`

at the point given by the vector `param`

.

This code was borrowed from the fBasics function, in the file ‘utils-hessian.R’ with slight modification. This was in turn borrowed from Kevin Sheppard's Matlab garch toolbox as implemented by Alexios Ghalanos in his rgarch package.

David Scott d.scott@auckland.ac.nz, Christine Yang Dong c.dong@auckland.ac.nz

`hyperbHessian`

and `summary.hyperbFit`

in
GeneralizedHyperbolic.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
### Consider Hessian of log(1 + x + 2y)
### Example from Lang: A Second Course in Calculus, p.74
fun <- function(param){
x <- param[1]
y <- param[2]
return(log(1 + x + 2*y))
}
### True value of Hessian at (0,0)
trueHessian <- matrix( c(-1,-2,
-2,-4), byrow = 2, nrow = 2)
trueHessian
### Value from tsHessian
approxHessian <- tsHessian(c(0,0), fun = fun)
approxHessian
maxDiff <- max(abs(trueHessian - approxHessian))
### Should be approximately 0.045
maxDiff
``` |

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