Robust case-weighting for multipurpose establishment surveys.

Abstract

Case-weighting or assigning a unique weight to each sample unit is a popular method of sample weighting when internal consistency of the survey estimates is paramount. If in addition external constraints on key variables (the survey benchmarks) must also be met, then case-weights computed via generalised least squares, based on an assumed linear regression model for the survey variables, can be used. Unfortunately, this method of weighting can lead to negative case-weights. It is also susceptible to bias if the linear model is misspecified. This article proposes a modified method of linear regression-based case-weighting which ensures positive weights via use of a ridging procedure, and model misspecification robustness via the inclusion of a nonparametric regression bias correction factor. Empirical results which illustrate the gains from the new method of weighting are presented