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Activity Number:
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88
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2007 : 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 - #307689 |
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Title:
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Studying Interactions Without Multivariate Modeling
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Author(s):
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Alain Hecq*+ and Gianluca Cubadda and Franz Palm
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Companies:
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University of Maastricht and University of Tor Vergata and University of Maastricht
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Address:
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Dept. of Quantitative E, Maastricht, International, 6200 MD, Netherlands
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Keywords:
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Common cycles ; ARIMA ; Cointegration ; Panel data ; VAR
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Abstract:
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We propose in this paper an approach that aims at providing guidance for checking the data admissibility of non-stationary multivariate time series models (VAR or VARMA) and their implied individual ARIMA specifications. In particular we show that the presence of different kinds of common cyclical features restrictions, leading to reduced rank in the short run dynamics, explains to a large extent why we can identify such parsimonious univariate ARIMA models in applied research, a paradox that the profession had problems to explain before. This allows us to develop a new strategy for studying interactions between variables without modeling these relationships in a multivariate setting. Indeed, we develop tools to study features of individual time series with the aim to infer features of the complete system, as individual series keep a print of the system as a whole.
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