Evaluation of Generalized Variance Functions in the Analysis of Complex Survey Data

Cho MoonJung (), Eltinge John L. (), Gershunskaya Julie () and Huff Larry ()
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Cho MoonJung: U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. N.E., Washington, DC 20212, U.S.A
Eltinge John L.: U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. N.E., Washington, DC 20212, U.S.A
Gershunskaya Julie: U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. N.E., Washington, DC 20212, U.S.A
Huff Larry: U.S. Bureau of Labor Statistics, 2 Massachusetts Ave. N.E., Washington, DC 20212, U.S.A

Journal of Official Statistics, 2014, vol. 30, issue 1, 63-90

Abstract: Two sets of diagnostics are presented to evaluate the properties of generalized variance functions (GVFs) for a given sample survey. The first set uses test statistics for the coefficients of multiple regression forms of GVF models. The second set uses smoothed estimators of the mean squared error (MSE) of GVF-based variance estimators. The smooth version of the MSE estimator can provide a useful measure of the performance of a GVF estimator, relative to the variance of a standard design-based variance estimator. Some of the proposed methods are applied to sample data from the Current Employment Statistics survey.

Keywords: Complex sample design; degrees of freedom; design-based inference; model-based inference; quarterly census of employment and wages; superpopulation model; U.S. current employment statistics (CES) survey; variance estimator stability (search for similar items in EconPapers)
Date: 2014
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:offsta:v:30:y:2014:i:1:p:63-90:n:4

DOI: 10.2478/jos-2014-0004

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