MLE.gpd: Maximum Likelihood estimation of the Generalized Pareto...

In freakonometrics/TopIncomes: Estimation of Top Incomes Using Pareto Models

Description

Maximum Likelihood estimation of the Generalized Pareto distribution, with weights

Usage

MLE.gpd(data, weights = rep(1, length(x)), threshold = NA,
  nextremes = NA, method = "ml", information = c("observed",
  "expected"), ...)

Arguments

data	numeric, vector of observations
weights	numeric, vector of weights (default = 1)
method	character, method used for inference ("ml" for maximum likelihood)
information	character (not used)
threhol	numeric, threshold parameter of the Generalized Pareto distribution (mu)
nextrmes	numeric, number of largest values considered (integer)

Value

a list xi the tail index, mu the threshold, "beta" the scaling coefficient, and k the number of observations above mu

References

Charpentier & Flachaire (2019) Pareto Models for Top Incomes hal-02145024

Examples

set.seed(123)
x <- rpareto1(100, 1, 1.5)
w <- rgamma(100,10,10)
estim <- MLE.gpd(data=x, weights=w, threshold=1)
estim$par.ests

freakonometrics/TopIncomes documentation built on July 7, 2019, 8:06 a.m.