break.var.lps

Function to calculate loss function at the estimated change points

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

break.var.lps function

<dl><dt>data</dt>
<dd>A n by p dataset, n is the number of observations, p is the number of variables</dd>
<dt>pts</dt>
<dd>a vector of change points</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>a positive number, indicating the value of constraint space of low rank component, default is 0.25</dd></dl>

Arguments

Auxiliary function to calculate loss at the estimated change points — break.var.lps

<dl>

<dt>data</dt>
<dd>A n by p dataset, n is the number of observations, p is the number of variables</dd>


<dt>pts</dt>
<dd>a vector of change points</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>a positive number, indicating the value of constraint space of low rank component, default is 0.25</dd>

</dl>

break.var.lps: Auxiliary function to calculate loss at the estimated change points

Description

Usage

Value

Arguments