radf_common_cv simulates critical values for radf_common
under its own null (no common explosive factor): an N-column panel
of independent random walks, extracted to one principal component
and tested exactly as radf_common does. Unlike
radf_mc_cv – which has no dependence on panel width and was
shown by independent validation to be badly undersized as a stand-in for
radf_common's own null once N grows past a handful of
series – this null distribution does depend on N, so N
must match the panel radf_common was actually run on.
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) that
radf_commonwill be run on – the critical value depends on this, unlikeradf_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. Either NULL or an integer will be used in a call to
set.seedbefore simulation. If set, the value is saved as "seed" attribute of the returned value. The default, NULL, will not change rng state, and return .Random.seed as the "seed" attribute. Results are reproducible across the parallel and non-parallel option when the same seed is used.
Value
A list with adf_cv, sadf_cv, gsadf_cv,
badf_cv, bsadf_cv – the same shape as radf_mc_cv's
return value, so it can be used as a drop-in cv argument for
datestamp/tidy/autoplot on a radf_common
result.
![[Experimental]](figures/lifecycle-experimental.svg)
References
Chen, Y., Phillips, P. C. B., & Shi, S. (2023). Common Bubble Detection in Large Dimensional Financial Systems. Journal of Financial Econometrics, 21(4), 989-1063.
Examples
# \donttest{
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 0.529 1.87 2.37
#> 2 95 0.834 2.10 2.79
#> 3 99 1.21 2.90 3.07
# }