PANEL SURVEY ESTIMATION IN THE PRESENCE OF LATE REPORTING AND NONRESPONSE

Abstract

Estimates from economic panel surveys are generally required to be published soon after the survey reference period, resulting in missing data due to late reporting as well as nonresponse. Estimators currently in use make some attempt to correct for the impact of missing data. However, these approaches tend to simplify the assumed nature of the missing data and often ignore a portion of the reported data for the reference period. Discrepancies between preliminary and revised estimates highlight the inability of the estimation methodology to correct for all error due to late reporting.

The current model for one economic panel survey, the Current Employment Statistics survey, is examined to identify factors related to potential model misspecification error, leading to identification of an extended model. An approach is developed to utilize all reported data from the current and prior reference periods, through missing data imputation. Two alternatives to the current models that assume growth rates are related to recent reported data and reporting patterns are developed, one a simple proportional model, the other a hierarchical fixed effects model. Estimation under the models is carried out and performance compared to that of the current estimator through use of historical data from the survey. Results, although not statistically significant, suggest the potential associated with use of reported data from recent time periods in the working model, especially for smaller establishments.

A logistic model for predicting likelihood of late reporting for sample units that did not report for preliminary estimates is also developed. The model uses a combination of operational, respondent, and environmental factors identified from a reporting pattern profile. Predicted conditional late reporting rates obtained under the model are compared to actual rates through use of historical information for the survey. Results indicate the appropriateness of the parameters chosen and general ability of the model to predict final reporting status. Such a model has the potential to provide information to survey managers for addressing late reporting and nonresponse.