Analysis

Diagnostics on hypothesis testing

Also: diagnostics.radf_obj

diagnostics

diagnostics(object, cv = NULL, ...)

diagnostics(object, cv = NULL, option = c("gsadf", "sadf"), sig_lvl = 95, ...)

Reports whether the null hypothesis of a unit root is rejected against the alternative of explosive behavior, for each series in a dataset.

Arguments

object	An object of class `obj`.
cv	An object of class `cv`.
...	Further arguments passed to methods.
option	Whether to apply the "gsadf" or "sadf" methodology (default = "gsadf").
sig_lvl	Significance level, one of 90, 95 or 99, that decides whether a series counts as "positive" (rejects the null). It does not depend on the test statistic chosen with `option`.

Value

A list with the series that reject the null hypothesis (positive) and the series that do not (negative), together with the significance level.

Details

diagnostics also stores a vector that takes the value 1 when there is a period of explosive behavior and 0 otherwise.

Examples

These examples are copied from the package's own documentation and are run by R CMD check on every release. The printed output (after #>) and the plots were produced by running them against the current package source.

# The default `cv` is fetched from the shared critical-value store
# (network on first use); pass `cv = radf_mc_cv(nrow(sim_data))` to stay offline
rsim_data <- radf(sim_data)
diagnostics(rsim_data)
#> Using precomputed critical values for `cv`.
#> 
#> ── Diagnostics (option = gsadf) ───────────────────────────────── Monte Carlo ──
#> 
#> psy1:     Rejects H0 at the 1% significance level
#> psy2:     Rejects H0 at the 1% significance level
#> evans:    Rejects H0 at the 1% significance level
#> div:      Cannot reject H0 
#> blan:     Rejects H0 at the 1% significance level

diagnostics(rsim_data, option = "sadf")
#> Using precomputed critical values for `cv`.
#> 
#> ── Diagnostics (option = sadf) ────────────────────────────────── Monte Carlo ──
#> 
#> psy1:     Rejects H0 at the 1% significance level
#> psy2:     Rejects H0 at the 1% significance level
#> evans:    Rejects H0 at the 1% significance level
#> div:      Rejects H0 at the 10% significance level
#> blan:     Rejects H0 at the 1% significance level

# Gate on the 90\% critical value instead of the 95\% default
diagnostics(rsim_data, sig_lvl = 90)
#> Using precomputed critical values for `cv`.
#> 
#> ── Diagnostics (option = gsadf) ───────────────────────────────── Monte Carlo ──
#> 
#> psy1:     Rejects H0 at the 1% significance level
#> psy2:     Rejects H0 at the 1% significance level
#> evans:    Rejects H0 at the 1% significance level
#> div:      Cannot reject H0 
#> blan:     Rejects H0 at the 1% significance level