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Activity Number:
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221
- Analysis of Big Dynamically Dependent Data
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2018 : 2:00 PM to 3:50 PM
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Sponsor:
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Business and Economic Statistics Section
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Abstract #326911
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Presentation
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Title:
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A Factor Augmented Vector Autoregressive Model Under High-Dimensional Scaling
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Author(s):
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George Michailidis* and Jiahe Lin
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Companies:
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University of Florida and University of Michigan
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Keywords:
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Factor models;
vector autoregressions;
identifiability;
estimation;
inference
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Abstract:
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Vector Autoregressive Models (VAR) are widely used in applied economics and finance. In this talk we consider a VAR model augmented with dynamically evolving factors. The time series modeled as a VAR, together with the dynamic factors relate to a large number of other time series hat aid in the identifiability of the model parameters. We investigate the identifiability of such models, as well as estimation and inference issues under high-dimensional scaling. The performance of the proposed methods is assessed through synthetic data and the methodology is illustrated on a large set of US macroeconomic variables.
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Authors who are presenting talks have a * after their name.
