VARDetect0.1.8 package

Multiple Change Point Detection in Structural VAR Models

first.step.blocks.group

block fused sparse group lasso step (first step).

first.step.blocks

block fused lasso step (first step for BSS).

second.step.local

local screening step (second step).

shrinkage.lr

Shrinkage function for low-rank soft-thresholding

shrinkage

Shrinkage function for sparse soft-thresholding

simu_lstsp

Function to deploy simulation with LSTSP algorithm

simu_tbss

Simulation function for TBSS algorithm

backward.selection

Backward selection function for the second screening step

BIC

BIC and HBIC function

block.finder

cluster the points by neighborhood size a_n

break.var.local.new

Compute local loss function.

break.var.lps

Auxiliary function to calculate loss at the estimated change points

f.func

Main loss function for quardratic loss

pred

Prediction function (single observation)

print.VARDetect.result

Function to print the change points estimated by VARDetect

prox.nuclear.func.fLS

Proximal function for nuclear norm penalty

prox.nuclear.func

Proximal function with nuclear norm penalty updating

prox.sparse.func

Proximal function with l1-norm penalty updating

Q.func

An auxiliary function in FISTA algorithm

remove.extra.pts

helper function for detection check

second.step.detect

Backward elimination algorithm for screening in the second step

cv.detect.LpS

Single change point detection for low-rank plus sparse model with cros...

cv.separate

cross-validation index function, separate train and test sets

cv.tuning.selection

a function to apply cross-validation to select tuning parameter by min...

detect.LpS

Single change point detection for low-rank plus sparse model structure

detection_check

Function for detection performance check

eval_func

Evaluation function, return the performance of simulation results

first.step.detect

First step rolling window function

fista.LpS

A function to solve low rank plus sparse model estimation using FISTA ...

fista.nuclear

A helper function for implementing FISTA algorithm to estimate low-ran...

gradf.func

Gradient function of quardratic loss

hausdorff_check

Function for Hausdorff distance computation

lag_selection

Select the lag of the VAR model using total BIC method

lstsp

Main function for the low rank plus sparse structure VAR model

nuclear.pen

Nuclear norm penalty for low-rank component

obj.func

Objective function

plot.VARDetect.result

Plotting the output from VARDetect.result class

plot_density

Function to plot the sparsity levels for estimated model parameters

plot_granger

Function to plot Granger causality networks

plot_matrix

Plot the AR coefficient matrix

pred.block

Prediction function (block)

simu_var

Generate VAR(p) model data with break points

sparse.pen

L1-norm penalty for sparse component

summary.VARDetect.result

Function to summarize the change points estimated by VARDetect

summary.VARDetect.simu.result

A function to summarize the results for simulation

tbss

Block segmentation scheme (BSS).

third.step.exhaustive.search

Exhaustive search step (third step).

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>.

  • Maintainer: Yue Bai
  • License: GPL-2
  • Last published: 2024-06-15