Abstract
Daniell Toth and Stuart Scott (2006)
"Variance Estimation for Noise Components in
Time Series from a Survey"
Models for economic time series of the form y=trend + seasonal + irregular
typically assume each term is stochastic with a noise component. A
fourth noise component enters the picture when the series is observed
from a survey. Chen, Wong, Morry, and Fung (2003) compared method
of moments and spectral estimates of "combined error" autocovariances
in X-11 seasonal adjustment. This paper revisits the topic both
with and without the use of external sampling error information. For
comparison, we use simulated data generated from structural models—
-as done by Chen et al.—-and sampling error models—-suggested by the
Bureau of Labor Statistics employment and unemployment series. We
investigate whether prior smoothing in this system adds stability to the
estimation. We also address selecting a "cutoff" value for the number of
autocovariance terms needed.
Last Modified Date: January 9, 2007
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Bureau of Labor Statistics
