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Accounting for Uncertainty About Variances in Small Area Estimation
Accounting for Uncertainty About Variances in Small Area Estimation
Introduction
This paper considers different approaches to dealing with uncertainty in the context of a particular application: estimating annual poverty rates of school-aged (5-17) children for the states of the U.S. using data from the Current Population Survey (CPS). For this problem Fay and Train (1997) developed a Fay-Herriot model for each year where, for each of m=51 “states” i (including the District of Columbia as a “state”), y is the direct CPS estimate, Y the true poverty rate, and x includes a constant term and three variables derived from administrative sources. (Actually, ratios differing slightly from true poverty rates were modeled.) U.S. Internal Revenue Service income tax return files supplied two variables: an analogue to state child poverty rates and also state rates of nonfiling for income taxes. Data from the U.S. Department of Agriculture were used to develop a variable reflecting state participation rates in the food stamp poverty assistance program.
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Page Last Revised - October 8, 2021
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