Causal vector autoregression enhanced with covariance and order selection

Medientyp: E-Artikel

Titel: Causal vector autoregression enhanced with covariance and order selection

Beteiligte: Bolla, Marianna [Verfasser:in]; Ye, Dongze [Verfasser:in]; Wang, Haoyu [Verfasser:in]; Ma, Renyuan [Verfasser:in]; Frappier, Valentin [Verfasser:in]; Thompson, William [Verfasser:in]; Donner, Catherine [Verfasser:in]; Baranyi, Máté [Verfasser:in]; Abdelkhalek, Fatma [Verfasser:in]

Erschienen: 2023

Sprache: Englisch

DOI: 10.3390/econometrics11010007

Identifikator:

Schlagwörter: block Cholesky decomposition ; causality along a DAG ; covariance selection ; order selection ; structural vector autoregression ; Aufsatz in Zeitschrift

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Beschreibung: 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≥1 of the fitted CVAR(p) model, order selection is performed with various information criteria.

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