first.step.detect: First step rolling window function

In VARDetect: Multiple Change Point Detection in Structural VAR Models

First step rolling window function

Description

Rolling window scheme function for the first step

Usage

first.step.detect(
  data,
  h,
  step.size = NULL,
  lambda,
  mu,
  alpha_L = 0.25,
  skip = 3,
  lambda.1.seq = NULL,
  mu.1.seq = NULL,
  cv = FALSE,
  nfold = NULL,
  verbose = FALSE
)

Arguments

data	the whole data matrix
h	window size
step.size	rolling step size, default is NULL. If Null, the step size is 1/4 of the window size
lambda	a 2-d vector of tuning parameters for sparse components, available when cv is FALSE
mu	a 2-d vector of tuning parameters for low rank components, available when cv is FALSE
alpha_L	a numeric value, indicates the size of constraint space of low rank component
skip	the number of observations we should skip near the boundaries, default is 3
lambda.1.seq	the sequence of sparse tuning parameter to the left segment, only available when cv is TRUE
mu.1.seq	the sequence of low rank tuning, only available for cv is TRUE
cv	a boolean argument, indicates whether use cross validation or not
nfold	a positive integer, indicates the number of folds of cross validation
verbose	if TRUE, then all information for current stage are printed

Value

A vector which includes all candidate change points selected by rolling window

VARDetect documentation built on May 10, 2022, 9:07 a.m.