cv.separate

a function to setup training and testing datasets, select data point in test every nfold time 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

cv.separate function

<dl><dt>period</dt>
<dd>an input dataset with size of n by p</dd>
<dt>nfold</dt>
<dd>an positive integer, indicates the number of folds in cv</dd></dl>

Arguments

cross-validation index function, separate train and test sets — cv.separate

<dl>

<dt>period</dt>
<dd>an input dataset with size of n by p</dd>


<dt>nfold</dt>
<dd>an positive integer, indicates the number of folds in cv</dd>

</dl>

cv.separate: cross-validation index function, separate train and test sets

Description

Usage

Value

Arguments