CUSUM Statistic Simulation
Simulates multiple realizations of the CUSUM statistic.
sim_Vn_stat(size, kn = function(n) { 1 }, tau = 0,
use_kernel_var = FALSE, kernel = "ba", bandwidth = "and",
n = 500, gen_func = rnorm, args = NULL, parallel = FALSE)size |
Number of realizations to simulate |
kn |
A function returning a positive integer that is used in the definition of the trimmed CUSUSM statistic effectively setting the bounds over which the maximum is taken |
tau |
The weighting parameter for the weighted CUSUM statistic (defaults to zero for no weighting) |
use_kernel_var |
Set to |
kernel |
If character, the identifier of the kernel function as used in
the cointReg (see documentation for
|
bandwidth |
If character, the identifier of how to compute the bandwidth
as defined in the cointReg package (see
documentation for |
n |
The sample size for each realization |
gen_func |
The function generating the random sample from which the statistic is computed |
args |
A list of arguments to be passed to |
parallel |
Whether to use the foreach and doParallel packages to parallelize simulation (which needs to be initialized in the global namespace before use) |
This differs from sim_Vn() in that the long-run variance is estimated
with this function, while sim_Vn() assumes the long-run variance is
known. Estimation can be done in a variety of ways. If use_kernel_var
is set to TRUE, long-run variance estimation using kernel-based
techniques will be employed; otherwise, a technique resembling standard
variance estimation will be employed. Any technique employed, though, will
account for the potential break points, as described in
Rice et al. (). See the documentation for
stat_Vn for more details.
The parameters kernel and bandwidth control parameters for
long-run variance estimation using kernel methods. These parameters will be
passed directly to stat_Vn.
Versions of the CUSUM statistic, such as the weighted or trimmed statistics,
can be simulated with the function by passing values to kn and
tau; again, see the documentation for stat_Vn.
A vector of simulated realizations of the CUSUM statistic
Andrews DWK (1991). “Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation.” Econometrica, 59(3), 817-858.
Rice G, Miller C, Horváth L (????). “A new class of change point test of Rényi type.” in-press.
CPAT:::sim_Vn_stat(100)
CPAT:::sim_Vn_stat(100, kn = function(n) {floor(0.1 * n)}, tau = 1/3,
use_kernel_var = TRUE, gen_func = CPAT:::rchangepoint,
args = list(changepoint = 250, mean2 = 1))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.