Bootstrapping the decomposed PaF income polarization index
paf2.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 computes the decomposed PaF index of Duclos, Esteban and Ray (2004) for a specific value of \(\alpha\). The decomposition is with respect to the deprivation and surplus components as suggested by Araar (2008). The PaF index, the deprivation and surplus components, and also their bootstrap estimates, the estimated bias and the estimated standard error of each, and the confidence intervals are returned.
Araar A. (2008). On the Decomposition of Polarization Indices: Illustrations with Chinese and Nigerian Household Surveys. CIRPEE Working Paper No. 08-06. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1136142
Duclos J. Y., Esteban, J. and Ray D. (2004). Polarization: concepts, measurement, estimation. Econometrica, 72(6): 1737--1772.
paf2, paf.boot
y <- rgamma(100, 10, 0.01)
paf2.boot(y, 0.25)
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