The Effect of Complementing Current Population Survey Data with American Community Survey Data
Jaingjaing Yang
Department of Statistics
Colorado State University
 
Monday, May 10, 2004
1pm
Room 006, Statistics

The M.S. oral examination of Jiangjiang Yang will take place Monday, May 10, 2004. The examination will begin with JJ’s seminar, “The ffect of complementing current population survey data with American community survey data,” at 1:00 p.m. in Room 006 of the Statistics Building. The oral examination, which is open to all interested faculty, will immediately follow the seminar presentation.

ABSTRACT
Traditionally the Bureau of the Census has relied mostly on the Current
Population Survey (CPS) to help estimate the labor force statistics.
However, the data from the CPS are not large enough in quantity. As a result BLS can only develop reliable estimates with the CPS at the national level or the state level. At the sub-state levels, BLS can rely little on the CPS due to its lack of size.
The American Community Survey (ACS) is a recently developed on-going
large-scale survey that the Census Bureau uses to collect census type data on some basic population characteristics. After the introduction of the ACS we now see an opportunity to combine the new source of data with what we have from the CPS in estimating the labor force statistics. We would need to incorporate the ACS data into the CPS framework to get the desired results.
In this paper we study and compare the Current Population Survey (CPS) and the American Community Survey (ACS). We construct an ACS model compatible with the structural CPS model that has already been established by the US Bureau of Labor Statistics (BLS). We then combine both the ACS and the CPS into one state-space model and evaluate its performance in estimation in comparison to the CPS model alone. More specifically, we want to compare the mean square errors of the estimates (predicted by the Kalman Filter) of the CPS-only model and the CPS-ACS combined model. We look at the ratio of the two mean square errors; and we use a factorial analysis to explore the relationships between the ratio and a number of error factors.

 

 


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