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Jeremy Knutson

U.S. Census Bureau



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Matthew Thompson

U.S. Census Bureau



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Katherine Jenny Thompson

U.S. Census Bureau



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469 – Official Statistics: Policy and Practice

Developing Variance Estimates for Products in the Economic Census

Sponsor: Government Statistics Section
Keywords: Economic Census, finite population Bayesian bootstrap, approximate Bayesian bootstrap, hot deck imputation, products

Jeremy Knutson

U.S. Census Bureau

Matthew Thompson

U.S. Census Bureau

Katherine Jenny Thompson

U.S. Census Bureau

The Economic Census collects data on the revenue obtained from products (product data) from all sampled units. In the 2017 Economic Census, missing product data will be imputed using hot deck imputation, and variance estimates for product data will be published for the first time. The majority of Economic Census sectors utilize a probability subsample of establishments and employ post-stratification to account for industry classification and sampling deficiencies. Product data pose unique challenges. Often sampled establishments elect not to provide any values (complete nonresponse) and many products are rarely reported. Consequently, the variance estimator must account for sampling variance, post-stratification, and imputation variance. Previous research examined the variance estimation effects of sampling variance and post-stratification separately from imputation variance. The recommended multiple imputation variance estimator combines the finite population Bayesian bootstrap (FPBB) with the approximate Bayesian bootstrap (ABB).

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