exuber
Volatility-robust (other routes)
Wild Bootstrap Critical Values for the SBZ Statistic
radf_sbz_cv
Replication record →
radf_sbz_cv(
data,
minw = NULL,
nboot = 499L,
kernel = c("gaussian", "uniform"),
h = NULL,
seed = NULL
)radf_sbz_cv performs the HLST (2016) wild bootstrap. It is the same algorithm as radf_wb_cv, applied to the WLS/kernel-volatility statistic of radf_sbz instead of the classic supDF statistic. It generates critical values that include the time-varying badf_cv and bsadf_cv boundary that datestamp and autoplot need, and not only the three scalar critical values that summary() uses.
Arguments
| data | A univariate or multivariate numeric time series object, a numeric
vector or matrix, or a data.frame. A column may have leading or trailing
NA values, which describes an unbalanced panel in which series enter or
exit the sample at different times. Those periods are filled with NA in
badf and bsadf and excluded from the adf, sadf and
gsadf of that series. Interior NA values (a gap in the middle of
a series) are not supported. When any series is padded in this way, the panel
statistics (bsadf_panel and gsadf_panel) are not available, and
the function returns NA for them with a warning. |
| minw | A positive integer. The minimum window size (default = , where T denotes the sample size). |
| nboot | A positive integer. Number of bootstraps (default = 500L). |
| kernel | Kernel for the spot-volatility estimator (eq. 6 of Harvey,
Leybourne & Zu 2019), "gaussian" (default, as in the paper) or
"uniform". |
| h | Bandwidth for the spot-volatility estimator. The default is leave-one-out cross-validation over the search range of the paper. |
| 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. |
Value
An object of class radf_cv/sbz_cv/wb_cv: a list with the critical values adf_cv, sadf_cv and gsadf_cv (one row per series) and badf_cv and bsadf_cv (one array per series, one row per recursion point).
Status
[Experimental]
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.
y <- sim_psy1(150, seed = 1)
res <- radf_sbz(y, minw = 20)
cv <- radf_sbz_cv(y, minw = 20, nboot = 200, seed = 1)
summary(res, cv = cv)
#>
#> ── Summary (minw = 20, lag = 0) ────────── Wild Bootstrap (SBZ) (nboot = 200) ──
#>
#> series1 :
#> # A tibble: 3 × 5
#> stat tstat `90` `95` `99`
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 adf 0.103 1.14 1.56 1.81
#> 2 sadf 3.95 2.13 2.48 3.24
#> 3 gsadf 4.49 2.60 2.92 3.50
datestamp(res, cv = cv)
#>
#> ── Datestamp (min_duration = 0) ──────────────────────── Wild Bootstrap (SBZ) ──
#>
#> series1 :
#> Start Peak End Duration Signal Ongoing
#> 1 64 82 102 38 positive FALSE
#> 2 109 111 113 4 positive FALSE
#> 3 114 114 115 1 positive FALSESee also
radf_sbz for the statistic that this function pairs with, and radf_sbz_union for the bundled union-of-rejections test against the classic supDF statistic. You cannot obtain that test from this function and radf_wb_cv separately (see the Details of radf_sbz_union for why).
Other critical values: radf_common_cv(), radf_mc_cv(), radf_recovery_cv(), radf_sb_cv(), radf_sign_cv(), radf_sign_dm_cv(), radf_tt_cv(), radf_wb_cv(), radf_wb_ps_cv()
References
Harvey, D. I., Leybourne, S. J., & Zu, Y. (2019). Testing explosive bubbles with time-varying volatility. Econometric Reviews, 38(10), 1131-1151.