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Abstract:
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Composite estimation in repeated surveys with rotating panels refers to methods of estimation which exploit correlations in the data in the sample overlap between survey times to improve current 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 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 is particularly efficient as the regression coefficients incorporate information from the samples of both survey times. Unlike other composite regression estimators, the proposed estimator does not require micro-matching of data in the common sample, and therefore is free of problems of estimation quality associated with it. It is also considerably more practical than other composite regression estimators and the traditional AK-composite estimator.
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