Affiliations: [a] Gabelli School of Business, Fordham University, New York, NY, USA | [b] Center for Statistical Research and Methodology, U.S. Census Bureau, Washington, DC, USA
Correspondence:
[*]
Corresponding author: Chaitra H. Nagaraja, Gabelli School of Business, Fordham University; 5 Columbus Circle, Rm 1116, New York, NY 10019, USA. Tel.: +1 212 636 6678; E-mail:cnagaraja@fordham.edu
Abstract: The rolling sample methodology of the American Community Survey leads to
Multi-Year Estimates that measure aggregate activity over one, three, or five years.
This paper introduces a novel, non-model-based method for
quantifying the impact of viewing multi-year estimates as functions of
single-year estimates belonging to the same time span. The method
is based on examining the changes to confidence interval coverage.
The interpretation of a multi-year estimate as the simple average of single-year
estimates is a viewpoint that underpins the published estimates of sampling variability. Therefore, it is vital to ascertain the extent to which this viewpoint is
valid. We apply our new methodology to data from the U.S. Census Bureau's Multi-Year
Estimates Study and demonstrate that viewing a multi-year estimate as the simple average
of single-year estimates typically results in substantial distortions to coverage; therefore, multi-year estimates should not be interpreted as averages, but merely as period estimates.
Keywords: Rolling sample, confidence interval, time series
