gini.md {acid}

Gini Coefficient for a Mixture of Income Distributions

Description

This function uses Monte Carlo methods to estimate the Gini coefficient for a mixture of two continuous income distributions and a point mass for zero-incomes.

Usage

gini.md(n, dist1, dist2, theta, 
p0, p1, p2, dist.para.table)

Arguments

n

sample size used to estimate the gini coefficient.

dist1

character string with the name of the first continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution.

dist2

character string with the name of the second continuous distribution used. Must be listed in dist.para.table. Must be equivalent to the respective function of that distribution, e.g. norm for the normal distribution.

theta

vector with the parameters of dist1 and dist2. Order must be the same as in the functions for the distributions.

p0

scalar with probability mass for the point mass.

p1

scalar with probability mass for dist1.

p2

scalar with probability mass for dist2.

dist.para.table

a table of the same form as dist.para.t with distribution name, function name and number of parameters.

Value

gini

the estimated Gini coefficient.

y

a vector with the simulated incomes to estimate the Gini coefficient.

stat

a vector with the simulated group the observation was chosen from. 0 is the point mass, 1 dist1 and 2 dist2.

Author(s)

Alexander Sohn

References

Cowell, F.A. (2000): Measurement of Inequality, in: Atkinson and Bourguignon (eds.), Handbook of Income Distribution, pp. 87-166, Elsevier, Amsterdam.

See Also

dist.para.t, gini

Examples

 
theta<-c(2,1,5,2)
x<- c(rnorm(500,2,1),rnorm(500,5,2))
gini(x)$Gini
data(dist.para.t)
gini.md(1000,"norm","norm",theta,0,0.5,0.5,dist.para.t)$gini