Regression Composite Estimation for Current Population Survey

• Activity Number: 10 - Challenges and Breakthroughs in Analyzing Big Survey Data
• Type: Invited
• Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
• Sponsor: Government Statistics Section
• Author(s): Yang Cheng and Daniel Bonnery and Partha Lahiri and Timothy Trudell*
• Companies: US Census Bureau and University of Maryland and Maryland Longitudinal Data System Center and University of Maryland, College Park and
• Keywords: AK Composite Estimator; Current Population Survey; Regression Composite Estimation; Sample Rotation; Mean Square Error

Abstract

The Current Population Survey (CPS) is a monthly household sample survey consisting of eight rotation groups, so that each selected household will be interviewed for 4 consecutive months and another 4 consecutive months after resting 8 consecutive months. A composite type estimator is adopted in the CPS for the last several decades to estimate monthly employment and unemployment levels and rates, which combines sample information from the current month survey and previous months using the fact that 75% households have data for two consecutive months. In this talk, we introduce regression composite estimation and apply this method to estimate unemployment rates from CPS data for the period 2006-2012. We develop a Monte Carlo simulation experiment in order to compare the proposed, survey-weighted direct, and the AK estimates with the simulated true employment rates. Some numerical studies are conducted to illustrate the effectiveness of the proposed method.