Abstract
John Eltinge, Julie Gershunskaya, and Larry Huff (2002) "Use of
Auxiliary Information to Evaluate a Synthetic Estimator."
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 Quarterly Census of 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.
Last Modified Date: January 6, 2004
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Bureau of Labor Statistics
