MV_critical_cp: Statistics-adapted values for extended minimum volatility...

In mlrv: Long-Run Variance Estimation in Time Series Regression

Statistics-adapted values for extended minimum volatility selection.

Description

Smoothing parameter selection for bootstrap tests for change point tests

Usage

MV_critical_cp(
  y,
  X,
  t,
  gridm,
  gridtau,
  cvalue = 0.1,
  B = 100L,
  lrvmethod = 1L,
  ind = 2L,
  rescale = 0L
)

Arguments

y	vector, as used in the Heter_LRV
X	matrix, covariates
t	vector, time points.
gridm	vector, a grid of candidate m's.
gridtau	vector, a grid of candidate tau's.
cvalue	double, 1-quantile for the calculation of bootstrap variance, default 0.1.
B	integer, number of iterations for the calculation of bootstrap variance
lrvmethod	integer, see also Heter_LRV
ind	integer, the type of kernel, see also Heter_LRV
rescale	bool, whether to rescale when positiveness of the matrix is not obtained. default 0

Value

a matrix of critical values

References

Bai, L., & Wu, W. (2024). Difference-based covariance matrix estimation in time series nonparametric regression with application to specification tests. Biometrika, asae013.

Examples

n = 300
t = (1:n)/n
data = bregress2(n, 2, 1) # time series regression model with 2 changes points
critical = MV_critical_cp(data$y, data$x,t,  c(3,4,5), c(0.2,0.25, 0.3))

mlrv documentation built on Sept. 11, 2024, 6:57 p.m.