Monte Carlo Critical Values

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

Usage

radf_mc_cv(n, minw = NULL, nrep = 1000L, seed = NULL, lag = 0)

radf_mc_distr(n, minw = NULL, nrep = 1000L, seed = NULL, lag = 0)

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.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.

lag

A non-negative integer. Number of lags in the auxiliary regression, as in radf.

Value

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

See also

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

Other critical values: radf_common_cv(), radf_recovery_cv(), radf_sb_cv(), radf_sbz_cv(), radf_sign_cv(), radf_sign_dm_cv(), radf_tt_cv(), radf_wb_cv(), radf_wb_ps_cv()

Examples

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

tidy(mc)
#> # A tibble: 3 × 4
#>   sig       adf  sadf gsadf
#>   <fct>   <dbl> <dbl> <dbl>
#> 1 90    -0.415   1.01  1.60
#> 2 95    -0.0611  1.36  1.90
#> 3 99     0.714   1.85  2.44

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

tidy(mc2)
#> # A tibble: 3 × 4
#>   sig      adf  sadf gsadf
#>   <fct>  <dbl> <dbl> <dbl>
#> 1 90    -0.545  1.00  1.60
#> 2 95    -0.233  1.30  1.90
#> 3 99     0.582  1.73  2.52

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

autoplot(mdist)


# Apply the critical values to actual data
rsim_data <- radf(sim_data, minw = 20)
autoplot(rsim_data, cv = mc2)

# }