Hidalgo-Seo Statistic Simulation
Simulates multiple realizations of the Hidalgo-Seo statistic.
sim_hs_stat(size, corr = TRUE, gen_func = rnorm, args = NULL, n = 500, parallel = FALSE, use_kernel_var = FALSE, kernel = "ba", bandwidth = "and")
size |
Number of realizations to simulate |
corr |
Whether long-run variance should be computed under the assumption of correlated residuals |
gen_func |
The function generating the random sample from which the statistic is computed |
args |
A list of arguments to be passed to |
n |
The sample size for each realization |
parallel |
Whether to use the foreach and doParallel packages to parallelize simulation (which needs to be initialized in the global namespace before use) |
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 |
If corr is TRUE, then the residuals of the data-generating
process are assumed to be correlated and the test accounts for this in
long-run variance estimation; see the documentation for stat_hs
for more details. Otherwise, the sample variance is the estimate for the
long-run variance, as described in Hidalgo and Seo (2013).
A vector of simulated realizations of the Hidalgo-Seo statistic
Andrews DWK (1991). “Heteroskedasticity and Autocorrelation Consistent Covariance Matrix Estimation.” Econometrica, 59(3), 817-858.
Hidalgo J, Seo MH (2013). “Testing for structural stability in the whole sample.” Journal of Econometrics, 175(2), 84 - 93. ISSN 0304-4076, doi: 10.1016/j.jeconom.2013.02.008, http://www.sciencedirect.com/science/article/pii/S0304407613000626.
CPAT:::sim_hs_stat(100)
CPAT:::sim_hs_stat(100, gen_func = CPAT:::rchangepoint,
args = list(changepoint = 250, mean2 = 1))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.