paf.boot: Bootstrapping the PaF income polarization index

In DER: Income Polarization Index

Bootstrapping the PaF income polarization index

Description

Bootstrapping the PaF income polarization index

Usage

paf.boot(y, a, R = 1000, ncores = 1)

Arguments

y	A numeric vector with income data.
a	The value of \alpha. This can be a number only, between 0.25 and 1.
R	The number of bootstrap resamples to perform.
ncores	The number of cores to use. If greater than 1, parallel computing will take place. It is advisable to use it if you have many observations and or many variables, otherwise it will slow down the process. The default is 1, meaning that code is executed serially.

Details

The function compute the PaF index of Duclos, Esteban and Ray (2004) for a specific value of \alpha, the alienation and identification components, the 1 + normalized covariance, and also their bootstrap estimates, the estimated bias, the estimated standard error of each and the percentile bootstrap confidence interval for the PaF index are returned.

Value

A list including:

boot	A matrix with the bootstrap estimates.
index	The estimates.
info	A matrix with: the bootstrap based estimates, the bootstrap estimated bias of the estimates, the bootstrap estimated standard errors of the estimates, and the 95% percentile bootstrap confidence intervals for each component.

Author(s)

Michail Tsagris and Christos Adam.

R implementation and documentation: Michail Tsagris [e-mail] and Christos Adam [e-mail].

References

Duclos J. Y., Esteban, J. and Ray D. (2006). Polarization: concepts, measurement, estimation. In The Social Economics of Poverty (pp. 54–102). Routledge.

Duclos J. Y., Esteban, J. and Ray D. (2004). Polarization: concepts, measurement, estimation. Econometrica, 72(6): 1737–1772.

See Also

paf, paf2.boot

Examples

y <- rgamma(100, 10, 0.01)
paf.boot(y, 0.25)

DER documentation built on Nov. 13, 2025, 1:07 a.m.