Use of Auxiliary Information to Evaluate a Synthetic Estimator

• Activity Number: 244
• Type: Topic Contributed
• Date/Time: Tuesday, August 13, 2002 : 2:00 PM to 3:50 PM
• Sponsor: Section on Survey Research Methods*
• Author(s): Julie Gershunskaya*+ and John Eltinge and Larry Huff
• Affiliation(s): U.S. Bureau of Labor Statistics and U.S. Bureau of Labor Statistics and U.S. Bureau of Labor Statistics
• Keywords: Analysis of variance ; Coefficient of determination ; Covered Employment and Wages (ES-202) Program ; Mean squared prediction error ; Small area estimation ; Small domain estimation

Abstract

The Bureau of Labor Statistics has considerable interest in estimation of total monthly employment for small domains defined by the intersection of metropolitan statistical area and major industrial division, based on data from the Current Employment Survey (CES). One of several possible elementary estimators is a synthetic estimator based on state-level changes in employment within a major industrial division. It is important to evaluate empirically the magnitude of the bias of this estimator, relative to the magnitude of the standard error of this estimator, and relative to the magnitudes of the biases and standard errors of other candidate-elementary small-domain estimators. This paper studies the extent to which this type of evaluation may be enhanced through the use of auxiliary data from the Covered Employment and Wages (ES-202) Program, a nominal census of employment that provides data several months after production of CES estimates. Principal attention is devoted to evaluation of components of mean-squared error attributable, respectively, to: 1.) lack of fit in the implicit synthetic model; 2.) sampling error in the CES data; and 3.) nonsampling error in the CES data.