Modified regression estimator for repeated business surveys with changing survey frames

Composite estimation is a technique applicable to repeated surveys with controlled overlap between successive surveys. This paper examines the modified regression estimators that incorporate information from previous time periods into estimates for the current time period. The range of modified regression estimators are extended to the situation of business surveys with survey frames that change over time, due to the addition of “births” and the deletion of “deaths”. Since the modified regression estimators can deviate from the generalized regression estimator over time, it is proposed to use a compromise modified regression estimator, a weighted average of the modified regression estimator and the generalised regression estimator. A Monte Carlo simulation study shows that the proposed compromise modified regression estimator leads to significant efficiency gains in both the point-in-time and movement estimates.