VARDetect (version 0.1.8)

lag_selection: Select the lag of the VAR model using total BIC method

Description

Select the lag of the VAR model (if the lag is unknown) using BIC method for total segments

Usage

lag_selection(
  data,
  method = c("sparse", "group sparse", "fLS"),
  group.case = c("columnwise", "rowwise"),
  group.index = NULL,
  lambda.1.cv = NULL,
  lambda.2.cv = NULL,
  mu = NULL,
  block.size = NULL,
  blocks = NULL,
  use.BIC = TRUE,
  an.grid = NULL,
  threshold = NULL,
  lag_candidates,
  verbose = FALSE
)

Value

selected lag for VAR series

select_lag

An integer no less than 1 represents the selected lag of time series.

Arguments

data

input data matrix, each column represents the time series component

method

method is sparse, group sparse and fixed lowrank plus sparse

group.case

two different types of group sparse, column-wise and row-wise, respectively.

group.index

specify group sparse index. Default is NULL.

lambda.1.cv

tuning parameter lambda_1 for fused lasso

lambda.2.cv

tuning parameter lambda_2 for fused lasso

mu

tuning parameter for low rank component, only available when method is set to "fLS".

block.size

the block size

blocks

the blocks

use.BIC

use BIC for k-means part

an.grid

a vector of an for grid searching.

threshold

a numeric argument, give the threshold for estimated model parameter matrices. Default is NULL.

lag_candidates

potential lag selection set

verbose

A Boolean argument, if TRUE, it provides detailed information. Default is FALSE

Examples

# \donttest{
nob <- 1000; p <- 15
brk <- c(floor(nob / 2), nob + 1)
m <- length(brk)
q.t <- 2 # the lag of VAR model for simulation
signals <- c(-0.8, 0.6, 0.4)
try <- simu_var(method = "sparse", nob = nob, k = p, brk = brk,
                signals = signals, lags_vector = c(1, 2),
                sp_pattern = "off-diagonal")
data <- try$series; data <- as.matrix(data)

# Apply lag selection to determine the lag for the given time series
lag_candi <- c(1, 2, 3, 4)
select_lag <- lag_selection(data = data,
                            method = "sparse", lag_candidates = lag_candi)
print(select_lag)
# }