Supervised factor modeling for high-dimensional linear time series

Motivated by Tucker tensor decomposition, this paper imposes low-rank structures to the column and row spaces of coefficient matrices in a multivariate infinite-order vector autoregression (VAR), which leads to a supervised factor model with two factor modelings being conducted to responses and pred...

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Vydáno v:Journal of econometrics Ročník 249; s. 105995
Hlavní autoři: Huang, Feiqing, Lu, Kexin, Zheng, Yao, Li, Guodong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.05.2025
Témata:
algorithms
Dimension reduction
econometrics
High-dimensional time series
Infinite-order VAR
macroeconomics
Tensor decomposition
time series analysis
Weak group sparsity
Tensor decomposition
Dimension reduction
Weak group sparsity
High-dimensional time series
Infinite-order VAR
ISSN:0304-4076
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Shrnutí:Motivated by Tucker tensor decomposition, this paper imposes low-rank structures to the column and row spaces of coefficient matrices in a multivariate infinite-order vector autoregression (VAR), which leads to a supervised factor model with two factor modelings being conducted to responses and predictors simultaneously. Interestingly, the stationarity condition implies an intrinsic weak group sparsity mechanism of infinite-order VAR, and hence a rank-constrained group Lasso estimation is considered for high-dimensional linear time series. Its non-asymptotic properties are discussed by balancing the estimation, approximation and truncation errors. Moreover, an alternating gradient descent algorithm with hard-thresholding is designed to search for high-dimensional estimates, and its theoretical justifications, including statistical and convergence analysis, are also provided. Theoretical and computational properties of the proposed methodology are verified by simulation experiments, and the advantages over existing methods are demonstrated by analyzing US quarterly macroeconomic variables.
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ISSN:0304-4076
DOI:10.1016/j.jeconom.2025.105995