Bootstrapping the PaF income polarization index
paf.boot(y, a, R = 1000, ncores = 1)A list including:
A matrix with the bootstrap estimates.
The estimates.
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.
A numeric vector with income data.
The value of \(\alpha\). This can be a number only, between 0.25 and 1.
The number of bootstrap resamples to perform.
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.
Michail Tsagris and Christos Adam.
R implementation and documentation: Michail Tsagris mtsagris@uoc.gr and Christos Adam econp266@econ.soc.uoc.gr.
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.
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.
paf, paf2.boot
y <- rgamma(100, 10, 0.01)
paf.boot(y, 0.25)
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