Improved methods for estimation in repeated surveys: Combining time series and calibration

Abstract

The paper investigates new estimation techniques for repeated surveys, focusing on improving the precision of finite population parameter estimates at the current time t by incorporating auxiliary time series and calibration methods. Repeated surveys generate temporally correlated estimates, which time series models capture effectively. Calibration further enhances estimation by adjusting estimators with auxiliary data, reducing variance, and improving precision. Several new estimators of a time-dependent finite population characteristic (usually the mean, which is used in various statistical analyses) at time t are developed and evaluated under diverse scenarios, considering factors such as the correlation between the errors of the target and auxiliary time series, sampling variance, number of surveys, and model complexity. Numerical results demonstrate that calibrated estimators, particularly those incorporating time series adjustments, achieve superior accuracy in high-correlation settings. Regression-based estimator also shows robust performance across varying conditions, while traditional estimators relying solely on survey data are less precise.