gradf.func

Gradient function of quardratic loss

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

gradf.func function

<dl><dt>x</dt>
<dd>A vector, or matrix, indicating the model parameter</dd>
<dt>AtA</dt>
<dd>A p by p Gram matrix for corresponding design matrix A</dd>
<dt>Atb</dt>
<dd>An inner product for design matrix A and corresponding matrix (vector) b</dd></dl>

Arguments

Gradient function of quardratic loss — gradf.func

<dl>

<dt>x</dt>
<dd>A vector, or matrix, indicating the model parameter</dd>


<dt>AtA</dt>
<dd>A p by p Gram matrix for corresponding design matrix A</dd>


<dt>Atb</dt>
<dd>An inner product for design matrix A and corresponding matrix (vector) b</dd>

</dl>

gradf.func: Gradient function of quardratic loss

Description

Usage

Value

Arguments