163 – The Census Bureau's Quest to Make Better Research-Driven Decisions for Economic Surveys
Evaluating Imputation and Estimation Procedures in a Survey of Wholesale Businesses
Martin Klein
U.S. Census Bureau
Joanna Fane Lineback
U.S. Census Bureau
Joseph L. Schafer
U.S. Census Bureau
The Monthly Wholesale Trade Survey (MWTS) provides estimates of change in inventories and sales for wholesale businesses in the United States. In previous work, we developed a new procedure for multiply imputing values of sales and inventories for nonrespondents based on a multivariate linear mixed model. We conducted a simulation study, generating an artificial population with frame data and two years of monthly sales and inventory figures, and we observed how the current MWTS Horvitz-Thompson and random group estimators performed over repeated samples with and without missing data. We discovered that the complete-data point estimates, variance estimates and interval estimates did not behave as large-sample normal theory suggests they should. Over repeated samples, point estimates were highly skewed and interval estimates had poor confidence coverage. In this paper, we review our findings to date and describe ongoing research into new estimation procedures for Bayesian finite-population inference with stratified samples from highly skewed populations.