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Abstract Details
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Activity Number:
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636
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Type:
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Invited
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Business and Economic Statistics Section
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| Abstract - #300063 |
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Title:
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Factor Modeling for High-Dimensional Time Series
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Author(s):
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Clifford Lam*+ and Qiwei Yao
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Companies:
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London School of Economics and London School of Economics
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Address:
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Department of Statistics, London, International, WC2A 2AE, UK
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Keywords:
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Common factor ;
Dimension reduction ;
nonstationary ;
Eigenanalysis ;
Multivariate time series
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Abstract:
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We introduce a statistical approach for factor modeling of high dimensional time series, with the viewpoint of dimension reduction. Our method can handle nonstationary factors or long-memory factors. However under stationary settings, the inference is simple in the sense that the estimation of the factor dimension and the loadings is resolved by an eigenanlysis of a non-negative definite matrix. We introduce a method for estimating the number of factors for the panel of time series observations, which involves comparing the eigenvalues of the non-negative definite matrix above. Asymptotic results are presented, and we illustrate our method numerically with both simulated and real data sets.
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