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Authors = Marianna Bolla ORCID = 0000-0003-0134-8669

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30 pages, 504 KiB  
Article
Causal Vector Autoregression Enhanced with Covariance and Order Selection
by Marianna Bolla, Dongze Ye, Haoyu Wang, Renyuan Ma, Valentin Frappier, William Thompson, Catherine Donner, Máté Baranyi and Fatma Abdelkhalek
Econometrics 2023, 11(1), 7; https://doi.org/10.3390/econometrics11010007 - 24 Feb 2023
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Abstract
A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations [...] Read more.
A causal vector autoregressive (CVAR) model is introduced for weakly stationary multivariate processes, combining a recursive directed graphical model for the contemporaneous components and a vector autoregressive model longitudinally. Block Cholesky decomposition with varying block sizes is used to solve the model equations and estimate the path coefficients along a directed acyclic graph (DAG). If the DAG is decomposable, i.e., the zeros form a reducible zero pattern (RZP) in its adjacency matrix, then covariance selection is applied that assigns zeros to the corresponding path coefficients. Real-life applications are also considered, where for the optimal order p1 of the fitted CVAR(p) model, order selection is performed with various information criteria. Full article
(This article belongs to the Special Issue High-Dimensional Time Series in Macroeconomics and Finance)
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