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Activity Number: 41 - Topics on Bayesian Inference
Type: Contributed
Date/Time: Monday, August 3, 2020 : 10:00 AM to 2:00 PM
Sponsor: IMS
Abstract #313888
Title: Autoregressive Models for Tensor-Valued Time Series
Author(s): Zebang Li*
Companies: Rutgers The State Univ of NJ
Keywords: Autoregressive; Tensor-valued Time Series; Multivariate Time Series; Kronecker Approximition
Abstract:

Modern time series analysis can involve tenor-valued observations. For example, the portfolios (see Fama-French model) can be formed based on the grouping of Size, Book-to-Market, and Operating Profitability, which lead to a three dimensional tensor of returns for each day or month. We propose an autoregressive model for the tensor-valued time series. Comparing with the vector autoregressive model, it leads to a substantial dimension reduction while preserves the tensor structure and admits corresponding interpretations. The model is extended to have multiple lag-one autoregressive terms to provide more flexibility. Estimation procedures of the coefficient matrices and the choice of the number of lag-one terms are investigated with the corresponding theoretical analysis. The performance of the model is demonstrated with simulated and real examples.


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