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A Bayesian Zero-One Inflated Beta Model for Small Area Shrinkage Estimation
A Bayesian Zero-One Inflated Beta Model for Small Area Shrinkage Estimation
Abstract
We evaluate revisions to a Bayesian beta regression model proposed in Wieczorek and Hawala (2011), for U.S. county poverty rates. For small areas, some of which have survey estimates of poverty rates of 0 or 1, a zero-one inflated rate model extends the beta distribution to allow for these extreme estimates. The addition of a model error term allows the model to produce shrinkage estimates. We can estimate the model parameters and shrinkage estimates for the small areas via Bayesian computation techniques. Using simulated draws from a “pseudo population” based on American Community Survey (ACS) data, we compare the results to ACS-like direct estimates and 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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