plot_granger

A function to plot Granger causal network for each segment via estimated sparse component. Note that if it has multiple lags, it only provides the first order Granger causality plot.

Implementations of Thresholded Block Segmentation Scheme (TBSS) and Low-rank plus Sparse Two Step Procedure (LSTSP) algorithms for detecting multiple changes in structural VAR models. The package aims to address the problem of change point detection in piece-wise stationary VAR models, under different settings regarding the structure of their transition matrices (autoregressive dynamics); specifically, the following cases are included: (i) (weakly) sparse, (ii) structured sparse, and (iii) low rank plus sparse. It includes multiple algorithms and related extensions from Safikhani and Shojaie (2020) <doi:10.1080/01621459.2020.1770097> and Bai, Safikhani and Michailidis (2020) <doi:10.1109/TSP.2020.2993145>.

Yue Bai

VARDetect

Multiple Change Point Detection in Structural VAR Models

Peiliang Bai

Abolfazl Safikhani

George Michailidis

plot_granger function

<dl><dt>est_mats</dt>
<dd>A list of numeric sparse matrices, indicating the estimated sparse components for each segment</dd>
<dt>threshold</dt>
<dd>A numeric positive value, used to determine the threshold to present the edges</dd>
<dt>layout</dt>
<dd>A character string, indicates the layout for the igraph plot argument</dd></dl>

Arguments

Function to plot Granger causality networks — plot_granger

<dl>

<dt>est_mats</dt>
<dd>A list of numeric sparse matrices, indicating the estimated sparse components for each segment</dd>


<dt>threshold</dt>
<dd>A numeric positive value, used to determine the threshold to present the edges</dd>


<dt>layout</dt>
<dd>A character string, indicates the layout for the igraph plot argument</dd>

</dl>

plot_granger: Function to plot Granger causality networks

Description

Usage

Value

Arguments

Examples