block.finder

helper function for determining the clusters

internal

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

block.finder function

<dl><dt>pts</dt>
<dd>vector of candidate change points</dd>
<dt>an</dt>
<dd>radius of the cluster</dd></dl>

Arguments

cluster the points by neighborhood size a_n — block.finder

<dl>

<dt>pts</dt>
<dd>vector of candidate change points</dd>


<dt>an</dt>
<dd>radius of the cluster</dd>

</dl>

block.finder: cluster the points by neighborhood size a_n

Description

Usage

Value

Arguments