multibreakeR (version 0.1.0)

Main: Main

Description

Entry point for the whole computation of the algorithm of Bai, Lumsdaine, and Stock (1998)

Usage

Main(
  mat.y,
  mat.x = NULL,
  trend = FALSE,
  intercept = TRUE,
  ci = c(0.9, 0.95, 0.99),
  est.mode = "OLS",
  iter = 3,
  aic.bic.mode = "AIC",
  q.max = 2,
  trim = 0.15,
  pos.break = FALSE
)

Value

A list of the vector of f-statistics, the maximum f-statistic retained, the confidence interval, the critical values, the break date, the original matrix of time series tested, the matrix with breaking and not breaking covariates, the index of the break in the time series, the size of the break (mean.shift), the optimal "AIC" or "BIC", a ggplot object (g1), and the trimmed dates.

Arguments

mat.y

The matrix object of time series

mat.x

The matrix of optional covariates

trend

Whether we add a trend. Default = FALSE

intercept

Whether the break test is on the intercept only. Default = TRUE

ci

A vector of confidence intervals. Default = c(0.9, 0.95, 0.99)

est.mode

Estimation mode. Can be "OLS", "FGLS", or "IGLS"

iter

Maximum number of iterations in the "IGLS" mode. Default to 3

aic.bic.mode

Can be "AIC" or "BIC" depending on the criterion chosen for the lag selection

q.max

Maximum lag tested for the AIC or BIC criterion

trim

Percentage for the trim value for the starting and ending window over which the algorithm is not tested. Default to 15%

pos.break

Whether we want to select the maximum positive break only and discard the negative ones. Default to FALSE

Examples

# \donttest{
data(example_data)
list.results <- Main(mat.y = example_data, q = 2)# }