Abstract Details
Activity Number:
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334
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Type:
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Invited
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Date/Time:
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Tuesday, August 11, 2015 : 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 #314327
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View Presentation
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Title:
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Reduced-Rank Covariance Estimation in Vector Autoregressive Modeling
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Author(s):
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Richard A. Davis* and Pengfei Zang and Tian Zheng
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Companies:
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Columbia University and Columbia University and Columbia University
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Keywords:
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VAR models ;
Covariance estimation ;
matrix decomposition
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Abstract:
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We consider reduced-rank modeling of the white noise covariance matrix in a large dimensional vector autoregressive (VAR) model. We first propose the reduced-rank covariance estimator under the setting where independent observations are available. We derive the reduced-rank estimator based on a latent variable model for the observation vector and give the analytical form of its maximum likelihood estimate. Simulation results show that the reduced-rank covariance estimator outperforms two competing covariance estimators for estimating large dimensional covariance matrices from independent observations. Then we describe how to integrate the proposed reduced-rank estimator into the fitting of large dimensional VAR models, where we consider two scenarios that require different model fitting procedures. In the VAR modeling context, our reduced-rank covariance estimator not only provides interpretable descriptions of the dependence structure of VAR processes but also leads to improvement in model-fitting and forecasting over unrestricted covariance estimators. Two real data examples are presented to illustrate these fitting procedures.
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Authors who are presenting talks have a * after their name.
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