Methods for Analyzing Binned Income Data

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Description

Methods for model selection, model averaging, and calculating metrics, such as the Gini, Theil, Mean Log Deviation, etc, on binned income data where the topmost bin is right-censored. We provide both a non-parametric method, termed the bounded midpoint estimator (BME), which assigns cases to their bin midpoints; except for the censored bins, where cases are assigned to an income estimated by fitting a Pareto distribution. Because the usual Pareto estimate can be inaccurate or undefined, especially in small samples, we implement a bounded Pareto estimate that yields much better results. We also provide a parametric approach, which fits distributions from the generalized beta (GB) family. Because some GB distributions can have poor fit or undefined estimates, we fit 10 GB-family distributions and use multimodel inference to obtain definite estimates from the best-fitting distributions. We also provide binned income data from all United States of America school districts, counties, and states.

Details

Package: binequality
Type: Package
Version: 0.6.1
Date: 2014-09-14
License: GPL (>= 3.0)

The datasets are: state_bins, county_bins, and school_district_bins.

The main functions are: fitFunc, run_GB_family, getMids, getQuantilesParams, giniCoef, LRT, makeFitComb, makeInt, makeIntWeight, makeWeightsAIC, mAvg, midStats, MLD, modelAvg, paramFilt, and theilInd.

Type ?<object> to learn more about these objects, e.g. ?state_bins

Type ?<function> to see examples of the function's use, e.g. ?getMids

Author(s)

Samuel V. Scarpino, Paul von Hippel, and Igor Holas

Maintainer: Samuel V. Scarpino <scarpino@utexas.edu>

References

FIXME - references

See Also

gamlss

Examples

1
#FIXME, write the example run of states here