High-dimensional low-rank tensor autoregressive time series modeling

DOI10.1016/j.jeconom.2023.105544arXiv2101.04276MaRDI QIDQ6152591

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Publication date: 13 February 2024

Published in: Journal of Econometrics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.04276

zbMATH Keywords

tensor decompositionnuclear normhigh-dimensional time seriesglobal trade flowsnon-convex tensor regressiontensor-valued time series

Mathematics Subject Classification ID

Statistics (62-XX) Game theory, economics, finance, and other social and behavioral sciences (91-XX)

Cites Work

• Unnamed Item
• Tensor Decompositions and Applications
• Regularized estimation in sparse high-dimensional time series models
• Autoregressive models for matrix-valued time series
• Factor modeling for high-dimensional time series: inference for the number of factors
• Estimation of (near) low-rank matrices with noise and high-dimensional scaling
• Network vector autoregression
• An optimal statistical and computational framework for generalized tensor estimation
• Convex regularization for high-dimensional multiresponse tensor regression
• High-dimensional VARs with common factors
• Tensor completion and low-n-rank tensor recovery via convex optimization
• A Multilinear Singular Value Decomposition
• Matrix Variate Regressions and Envelope Models
• Low Rank and Structured Modeling of High-Dimensional Vector Autoregressions
• Non-convex Optimization for Machine Learning
• Finite-time analysis of vector autoregressive models under linear restrictions
• Factor Models for High-Dimensional Tensor Time Series
• High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition

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