exuber (version 1.0.2)

radf_mc_cv: Monte Carlo Critical Values

Description

radf_mc_cv computes Monte Carlo critical values for the recursive unit root tests. radf_mc_distr computes the distribution.

Usage

radf_mc_cv(n, minw = NULL, nrep = 1000L, seed = NULL)
radf_mc_distr(n, minw = NULL, nrep = 1000L, seed = NULL)

Value

For radf_mc_cv a list that contains the critical values for ADF, BADF, BSADF and GSADF test statistics. For radf_mc_distr a list that contains the ADF, SADF and GSADF distributions.

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. Either NULL or an integer will be used in a call to set.seed before 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 different between the parallel and non-parallel option, even if they have the same seed.

See Also

radf_wb_cv for wild bootstrap critical values and radf_sb_cv for sieve bootstrap critical values

Examples

# \donttest{
# Default minimum window
mc <- radf_mc_cv(n = 100)

tidy(mc)

# Change the minimum window and the number of simulations
mc2 <- radf_mc_cv(n = 100, nrep = 600, minw = 20)

tidy(mc2)

mdist <- radf_mc_distr(n = 100, nrep = 1000)

autoplot(mdist)
# }