backward.selection

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

backward.selection function

<dl><dt>data</dt>
<dd>A n by p dataset matrix</dd>
<dt>pts</dt>
<dd>A numeric vector, which includes all candidate change points obtained from the first step</dd>
<dt>lambda</dt>
<dd>Tuning parameter for sparse component</dd>
<dt>mu</dt>
<dd>Tuning parameter for low-rank component</dd>
<dt>alpha_L</dt>
<dd>Constraint space for low rank component, default is 0.25</dd></dl>

Arguments

Backward selection function for the second screening step — backward.selection

<dl>

<dt>data</dt>
<dd>A n by p dataset matrix</dd>


<dt>pts</dt>
<dd>A numeric vector, which includes all candidate change points obtained from the first step</dd>


<dt>lambda</dt>
<dd>Tuning parameter for sparse component</dd>


<dt>mu</dt>
<dd>Tuning parameter for low-rank component</dd>


<dt>alpha_L</dt>
<dd>Constraint space for low rank component, default is 0.25</dd>

</dl>

Backward selection function for the second screening step

backward.selection: Backward selection function for the second screening step

Description

Usage

Value

Arguments