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A Bayesian Zero-One Inflated Beta Model for Estimating Poverty in U.S. Counties
A Bayesian Zero-One Inflated Beta Model for Estimating Poverty in U.S. Counties
Abstract
We propose and evaluate a Bayesian beta regression model for U.S. county poverty rates. Such a rate model could be an improvement to the U.S. Census Bureau's current small-area poverty approach of linearly modeling the logarithm of poverty levels. For small areas, some of which may have estimates of no poverty or all poverty, a zero-one inflated rate model can usefully account for estimated rates of 0 or 1. Using Bayesian computation techniques, we estimate the parameters of a zero-one inflated beta regression model. We compare the results to the Census Bureau's current small-area model for county poverty estimation.
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Page Last Revised - October 8, 2021
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