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Activity Number:
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232
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 8, 2006 : 8:30 AM to 10:20 AM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #306397 |
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Title:
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Variance Estimation for Noise Components in Time Series from a Survey
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Author(s):
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Daniell Toth*+ and Stuart Scott
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Companies:
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Bureau of Labor Statistics and Bureau of Labor Statistics
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Address:
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2 Massachusettes Ave., NE, Washington, DC, 20212-0001,
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Keywords:
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seasonal adjustment ; sampling error ; structural models ; X-11
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Abstract:
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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.
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