Causal Vector Autoregression Enhanced with Covariance and Order Selection
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 of the fitted CVAR model, order selection is performed with various information criteria.
Cite
@article{arxiv.2211.14203,
title = {Causal Vector Autoregression Enhanced with Covariance and Order Selection},
author = {Marianna Bolla and Dongze Ye and Haoyu Wang and Renyuan Ma and Valentin Frappier and William Thompson and Catherine Donner and Máté Baranyi and Fatma Abdelkhalek},
journal= {arXiv preprint arXiv:2211.14203},
year = {2022}
}