Critical Values for the Common-Bubble (PCA + PSY) Test

radf_common_cv(n, N, minw = NULL, nrep = 1000L, seed = NULL)

radf_common_cv simulates critical values for radf_common under its own null of no common explosive factor. The null is a panel of N independent random walks, from which one principal component is extracted and tested exactly as in radf_common. Independent validation showed that radf_mc_cv, whose critical values do not depend on the panel width, is badly undersized as a stand-in for this null once N grows past a handful of series. The null distribution here does depend on N, so N must match the panel on which radf_common was run.

Arguments

n	A positive integer. The sample size (number of time periods).
N	A positive integer, at least 2. The panel width (number of series) on which radf_common will be run. The critical value depends on it, unlike that of radf_mc_cv.
minw	A positive integer. The minimum window size (default = $(0.01 + 1.8/\sqrt{T})T$, where T denotes the sample size).
nrep	A positive integer. The number of Monte Carlo simulations.
seed	An object specifying if and how the random number generator (rng) should be initialized. It is either NULL or an integer, which is passed to set.seed before the simulation. If you set it, the value is saved as the "seed" attribute of the returned value. The default, NULL, leaves the state of the rng unchanged and returns .Random.seed as the "seed" attribute. Results are reproducible across the parallel and the non-parallel option when you use the same seed.

Value

A list with adf_cv, sadf_cv, gsadf_cv, badf_cv and bsadf_cv. It has the same shape as the return value of radf_mc_cv, so you can use it as the cv argument of datestamp, tidy and autoplot on a radf_common result.

cv <- radf_common_cv(n = 100, N = 5, minw = 20, nrep = 200)
tidy(cv)
#> # A tibble: 3 × 4
#>   sig     adf  sadf gsadf
#>   <fct> <dbl> <dbl> <dbl>
#> 1 90    0.383  1.94  2.33
#> 2 95    0.621  2.30  2.53
#> 3 99    1.27   2.95  3.11