Abstract
Moon Jung Cho, John Eltinge, Julie Gershunskaya, and Larry Huff (2002) "Evaluation
of Generalized Variance Function Estimators for the U.S. Current
Employment Survey."
In applied work with generalized variance function models for sample
survey data, one generally seeks to develop and validate a model that is
relatively parsimonious and that produces variance estimators that are
approximately unbiased and relatively stable. This development and
validation work often begins with regression of initial variance
estimators (computed through standard design-based methods) on one or
more candidate explanatory variables. Evaluation of initial modeling
results is often complicated by correlation among the initial variance
estimators. This paper considers ways in which to address this problem,
with principal emphasis on three issues: 1.) approximate conditional or
unconditional independence of subsets of initial variance estimators;
2.) use of 1.) and additional conditions to evaluate the properties of
the estimators of the coefficients of a generalized variance function
model; and 3.) evaluation of the stability of the resulting variance
estimators. Some of the proposed diagnostics are applied to data from
the U.S. Current Employment Survey.
Last Modified Date: March 19, 2003
|
