A New Approach to Composite Estimation for Repeated Surveys with Rotating Panels

Abstract

Composite estimation in repeated surveys with rotating panels refers to methods of estimation that exploit correlations in the data in the sample overlap between survey times to improve the precision of estimates. In this article a novel approach to composite estimation is proposed, in which composite regression estimators of current totals for a number of key variables are generated from a simultaneous calibration of the sampling weights of the overlapping samples of the current and previous survey time. In this procedure, in addition to the usual calibration to known population totals, differences of estimates for the key variables based on the full sample and the common sample from the two consecutive times are calibrated to each other. The resulting multivariate composite regression estimator, which is constructed as an approximate best linear unbiased estimator, incorporates effectively information from the samples of both survey times for enhanced estimation efficiency. Unlike other composite regression estimators, the proposed estimator does not require micro-matching of data in the overlap sample, and, therefore, is free of potential issues associated with it. It is also considerably more practical than existing composite regression estimators and the traditional AK-composite estimator.