Autoregressive models for matrix-valued time series

DOI10.1016/j.jeconom.2020.07.015zbMath1471.62457arXiv1812.08916OpenAlexW3081388776MaRDI QIDQ109413

Han Xiao, Rong Chen, Dan Yang, Han Xiao, Dan Yang, Rong Chen

Publication date: May 2021

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

Full work available at URL: https://arxiv.org/abs/1812.08916

zbMATH Keywords

predictionKronecker productautoregressivemultivariate time seriesbilineareconomic indicatorsmatrix-valued time seriesnearest Kronecker product projection

Mathematics Subject Classification ID

Applications of statistics to economics (62P20) Factor analysis and principal components; correspondence analysis (62H25) Estimation in multivariate analysis (62H12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items (13)

Rank determination in tensor factor model ⋮ Matrix Autoregressive Spatio-Temporal Models ⋮ Editorial for the special issue on financial econometrics in the age of the digital economy ⋮ Robust online detection in serially correlated directed network ⋮ One-way or two-way factor model for matrix sequences? ⋮ Multivariate continuous-time autoregressive moving-average processes on cones ⋮ Determining the number of factors in constrained factor models via Bayesian information criterion ⋮ Asymptotic normality for eigenvalue statistics of a general sample covariance matrix when \(p/n \to \infty\) and applications ⋮ High-dimensional low-rank tensor autoregressive time series modeling ⋮ On singular values of data matrices with general independent columns ⋮ Autoregressive moving average model for matrix time series ⋮ Factor Models for High-Dimensional Tensor Time Series ⋮ tensorTS

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Regularized estimation in sparse high-dimensional time series models
• Global yield curve dynamics and interactions: a dynamic Nelson-Siegel approach
• Factor modeling for high-dimensional time series: inference for the number of factors
• Oracle inequalities for high dimensional vector autoregressions
• Estimation of (near) low-rank matrices with noise and high-dimensional scaling
• Autoregressive process modeling via the Lasso procedure
• Dynamic factors in the presence of blocks
• Diagnostic tests for multiple time series models
• Time series: theory and methods.
• Reduced-rank regression for the multivariate linear model
• The ubiquitous Kronecker product
• Factor models for matrix-valued high-dimensional time series
• Generalized autoregressive conditional heteroscedasticity
• NONCAUSAL VECTOR AUTOREGRESSION
• Estimation of latent factors for high-dimensional time series
• The Multivariate Portmanteau Statistic
• Modeling Multiple Times Series with Applications
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• A variable projection method for solving separable nonlinear least squares problems
• Vector linear time series models
• Ideals, Varieties, and Algorithms
• Constrained Factor Models
• Tensor Regression with Applications in Neuroimaging Data Analysis
• Structured lasso for regression with matrix covariates
• Determining the Number of Factors in Approximate Factor Models
• The Differentiation of Pseudo-Inverses and Nonlinear Least Squares Problems Whose Variables Separate
• Large Covariance Estimation by Thresholding Principal Orthogonal Complements
• The Generalized Dynamic Factor Model
• Estimating Linear Restrictions on Regression Coefficients for Multivariate Normal Distributions

This page was built for publication: Autoregressive models for matrix-valued time series