Monte Carlo Critical Values for the Recursively Demeaned Sign-Based Test
Source:R/radf_sign.R
radf_sign_dm_cv.RdSimulates the asymptotic null distribution of the statistic of
radf_sign_dm. As for radf_sign_cv, this distribution
does not depend on the volatility process (exact invariance, the analogue for
this variant of Theorem 2 of HLZ 2020), so it does not have to be recomputed for
each dataset.
Arguments
- n
A positive integer. The sample size.
- 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.seedbefore 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
An object of class radf_cv/sign_dm_cv/mc_cv
with the same structure as radf_mc_cv.
![[Experimental]](figures/lifecycle-experimental.svg)
References
Harvey, D. I., Leybourne, S. J., & Zu, Y. (2020). Sign-based unit root tests for explosive financial bubbles in the presence of deterministically time-varying volatility. Econometric Theory, 36(1), 122-169.
See also
Other critical values:
radf_common_cv(),
radf_mc_cv(),
radf_recovery_cv(),
radf_sb_cv(),
radf_sbz_cv(),
radf_sign_cv(),
radf_tt_cv(),
radf_wb_cv(),
radf_wb_ps_cv()
Examples
# \donttest{
cv <- radf_sign_dm_cv(n = 200, minw = 20)
tidy(cv)
#> # A tibble: 3 × 4
#> sig adf sadf gsadf
#> <fct> <dbl> <dbl> <dbl>
#> 1 90 0.861 2.54 3.36
#> 2 95 1.33 2.87 3.82
#> 3 99 1.97 3.53 4.88
# }