A Hierarchical Bayes Estimation of Poverty Rates

• Activity Number: 213
• Type: Invited
• Date/Time: Monday, July 30, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Survey Research Methods
• Author(s): Sam Hawala*+ and Partha Lahiri
• Companies: U.S. Census Bureau and University of Maryland
• Keywords: Small Area Estimation ; SAIPE ; Time-series and Cross-sectional Model ; Hierarchical Bayes Models ; Posterior Predictive Checks

Abstract

In practice many applications of small area models use a `Normal-Normal-Linear' assumption, i.e., a normality assumption for the design-based survey estimates and for the area-level random effects and a linear regression function relating the true parameters to available covariates. We compare the performance of rate models by slightly changing the assumptions and using internal and external checks. when area sample sizes are in the hundreds, empirical analyses using a 'Normal-t-Linear' to protect against outliers, or a seemingly reasonable `Beta-logistic' assumption for rates, show no gain over the `Normal-Normal-Linear' type model. However, the same type of analyses show additional benefit from including historical data through a cross-sectional and time series model. We use Monte Carlo Markov Chain (MCMC) to implement the proposed models, posterior predictive checks, as well as external checks for model comparisons.