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.
| Package: | binequality |
| Type: | Package |
| Version: | 1.0.4 |
| Date: | 2018-11-05 |
| 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
Von Hippel, P. T., Scarpino, S. V., & Holas, I. (2016). Robust estimation of inequality from binned incomes. Sociological Methodology, 46(1), 212-251.
# NOT RUN {
#FIXME, write the example run of states here
# }
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