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Activity Number: 138 - Modeling Applications for Backcasting, Nowcasting and Forecasting
Type: Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #303008
Title: Using American Community Survey Data to Improve Estimates from Smaller Surveys Through Bivariate Small Area Estimation Models
Author(s): Carolina Franco* and William Bell
Companies: U.S. Census Bureau and U.S. Census Bureau
Keywords: Bivariate Model; Fay-Herriot Model; Hierarchical Models; ACS

We demonstrate use of bivariate area-level models to improve small area estimates from one survey by borrowing strength from related estimates from a larger survey. In particular, we demonstrate the potential for borrowing strength from estimates from the American Community Survey (ACS), the largest U.S. household survey, to improve estimates from smaller U.S. surveys. For illustration we use, in conjunction with data from ACS, data from the National Health Interview Survey, the Survey of Income and Program Participation, and the Current Population Survey. To borrow information we propose use of a simple bivariate Gaussian model and also, for proportions, a bivariate binomial logit normal model. Simple theoretical calculations and the results from the examples show that substantial reductions in variances may be achieved by borrowing strength from the ACS via the bivariate models even without using regression covariates obtained from auxiliary sources. Theoretical calculations show how the extent of variance reduction depends on the characteristics of the underlying data.

Authors who are presenting talks have a * after their name.

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