Methods for Analyzing Binned Income Data
Methods for Analyzing Binned Income Data
A function to fit a parametric distribution to binned data.
A function to calculate the bin midpoints.
A function to extract the quantiles and parameters
Calculates the Gini coefficient from quantiles
A function to perform likelihood ratio tests
A function to transform a list into a dataframe
A function to create a survival object from bin counts.
A function to create a survival object from bin counts and normalized ...
A function to calculate AIC weights
A simple function to perfom model averaging using pre-calculated weigh...
A function to calculate statistics using bin midpoints
A function to calculate the MLD
A function to calculate model averages
A function to filter models based on estimated parameters
A function to fit a parametric distributions to binned data.
A function to calculate the SDL
A function to calculate the Theil
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.