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Abstract Details
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Activity Number:
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174
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Survey Research Methods
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| Abstract - #301679 |
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Title:
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A Bayesian Zero-One Inflated Beta Model for Estimating Poverty in U.S. Counties
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Author(s):
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Jerzy Wieczorek*+ and Sam Hawala
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Companies:
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U.S. Census Bureau and U.S. Census Bureau
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Address:
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4600 Silver Hill Road, Room 6H124C, Suitland, MD, 20233,
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Keywords:
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Small Area Estimates ;
SAIPE ;
MCMC ;
Beta Regression ;
Hierarchical Model
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Abstract:
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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 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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