Causal Vector Autoregression Enhanced with Covariance and Order Selection

Marianna Bolla; Dongze Ye; Haoyu Wang; Renyuan Ma; Valentin Frappier; William Thompson; Catherine Donner; Máté Baranyi; Fatma Abdelkhalek

arXiv · 2211.14203

Causal Vector Autoregression Enhanced with Covariance and Order Selection

Marianna Bolla·Dongze Ye·Haoyu Wang·Renyuan Ma·Valentin Frappier·William Thompson·Catherine Donner·Máté Baranyi·Fatma Abdelkhalek

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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 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 $p\ge 1$ of the fitted CVAR$(p)$ model, order selection is performed with various information criteria.

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Marianna Bolla, Dongze Ye, Haoyu Wang, Renyuan Ma, Valentin Frappier, William Thompson, Catherine Donner, Máté Baranyi, Fatma Abdelkhalek. 2022-11-25. Causal Vector Autoregression Enhanced with Covariance and Order Selection. https://arxiv.org/abs/2211.14203

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