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Activity Number:
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146
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economics Statistics Section
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| Abstract - #309896 |
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Title:
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Contemporaneous Aggregation of Time Series
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Author(s):
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Georgios Tripodis*+ and Jeremy Penzer
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Companies:
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University of Massachusetts Amherst and London School of Economics
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Address:
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1040 N Pleasant Street, Amherst, MA, 01002,
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Keywords:
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aggregation ; unobserved component models ; Kalman filter
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Abstract:
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This paper looks at the problem of contemporaneous aggregation of time series. Extensive work has been done in this area for ARIMA models. We consider the unobserved components framework. We consider different mechanisms for aggregating time series models and derive the conditions for identifiability for the aggregate series. We show that identifiability of the models for the component series is not sufficient for the identifiability of the model for the aggregate series. We also consider the case where there is no estimation error as well as the case of modeling an unknown process. For the case of the unknown process we provide recursions based on the Kalman filter that give the asymptotic variance of the estimated parameters.
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