nag_estim_gen_pareto (g07bfc) (PDF version)
g07 Chapter Contents
g07 Chapter Introduction
NAG Library Manual

NAG Library Function Document

nag_estim_gen_pareto (g07bfc)

 Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_estim_gen_pareto (g07bfc) estimates parameter values for the generalized Pareto distribution by using either moments or maximum likelihood.

2  Specification

#include <nag.h>
#include <nagg07.h>
void  nag_estim_gen_pareto (Integer n, const double y[], Nag_OptimOpt optopt, double *xi, double *beta, double asvc[], double obsvc[], double *ll, NagError *fail)

3  Description

Let the distribution function of a set of n observations
yi ,   i=1,2,,n  
be given by the generalized Pareto distribution:
Fy = 1- 1+ ξy β -1/ξ , ξ0 1-e-yβ , ξ=0;  
where
Estimates ξ^ and β^ of the parameters ξ and β are calculated by using one of:
The variances and covariance of the asymptotic Normal distribution of parameter estimates ξ^ and β^ are returned if ξ^ satisfies:
If the MLE option is exercised, the observed variances and covariance of ξ^ and β^ is returned, given by the negative inverse Hessian of L.

4  References

Hosking J R M and Wallis J R (1987) Parameter and quantile estimation for the generalized Pareto distribution Technometrics 29(3)
McNeil A J, Frey R and Embrechts P (2005) Quantitative Risk Management Princeton University Press

5  Arguments

1:     n IntegerInput
On entry: the number of observations.
Constraint: n>1.
2:     y[n] const doubleInput
On entry: the n observations yi, for i=1,2,,n, assumed to follow a generalized Pareto distribution.
Constraints:
  • y[i-1]0.0;
  • i=1 n y[i-1]>0.0.
3:     optopt Nag_OptimOptInput
On entry: determines the method of estimation, set:
optopt=Nag_PWM
For the method of probability-weighted moments.
optopt=Nag_MOM
For the method of moments.
optopt=Nag_MOMMLE
For maximum likelihood with starting values given by the method of moments estimates.
optopt=Nag_PWMMLE
For maximum likelihood with starting values given by the method of probability-weighted moments.
Constraint: optopt=Nag_PWM, Nag_MOM, Nag_MOMMLE or Nag_PWMMLE.
4:     xi double *Output
On exit: the parameter estimate ξ^.
5:     beta double *Output
On exit: the parameter estimate β^.
6:     asvc[4] doubleOutput
On exit: the variance-covariance of the asymptotic Normal distribution of ξ^ and β^. asvc[0] contains the variance of ξ^; asvc[3] contains the variance of β^; asvc[1] and asvc[2] contain the covariance of ξ^ and β^.
7:     obsvc[4] doubleOutput
On exit: if maximum likelihood estimates are requested, the observed variance-covariance of ξ^ and β^. obsvc[0] contains the variance of ξ^; obsvc[3] contains the variance of β^; obsvc[1] and obsvc[2] contain the covariance of ξ^ and β^.
8:     ll double *Output
On exit: if maximum likelihood estimates are requested, ll contains the log-likelihood value L at the end of the optimization; otherwise ll is set to -1.0.
9:     fail NagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.2.1.2 in the Essential Introduction for further information.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_INT
On entry, n=value.
Constraint: n>1.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
An unexpected error has been triggered by this function. Please contact NAG.
See Section 3.6.6 in the Essential Introduction for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 3.6.5 in the Essential Introduction for further information.
NE_OPTIMIZE
The optimization of log-likelihood failed to converge; no maximum likelihood estimates are returned. Try using the other maximum likelihood option by resetting optopt. If this also fails, moments-based estimates can be returned by an appropriate setting of optopt.
Variance of data in y is too low for method of moments optimization.
NE_REAL_ARRAY
On entry, y[value]=value.
Constraint: y[i-1]0.0 for all i.
NE_ZERO_SUM
The sum of y is zero within machine precision.
NW_PARAM_DIST
The asymptotic distribution of parameter estimates is invalid and the distribution of maximum likelihood estimates cannot be calculated for the returned parameter estimates because the Hessian matrix could not be inverted.
NW_PARAM_DIST_ASYM
The asymptotic distribution is not available for the returned parameter estimates.
NW_PARAM_DIST_OBS
The distribution of maximum likelihood estimates cannot be calculated for the returned parameter estimates because the Hessian matrix could not be inverted.

7  Accuracy

Not applicable.

8  Parallelism and Performance

nag_estim_gen_pareto (g07bfc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9  Further Comments

The search for maximum likelihood parameter estimates is further restricted by requiring
1+ ξ^yi β^ > 0 ,  
as this avoids the possibility of making the log-likelihood L arbitrarily high.

10  Example

This example calculates parameter estimates for 23 observations assumed to be drawn from a generalized Pareto distribution.

10.1  Program Text

Program Text (g07bfce.c)

10.2  Program Data

Program Data (g07bfce.d)

10.3  Program Results

Program Results (g07bfce.r)


nag_estim_gen_pareto (g07bfc) (PDF version)
g07 Chapter Contents
g07 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2015