Wild Bootstrap Critical Values for the SBZ Statistic

radf_sbz_cv performs the HLST (2016) wild bootstrap – the same algorithm as radf_wb_cv, applied to radf_sbz's WLS/kernel-volatility statistic instead of the classic supDF one – to generate critical values, including the time-varying badf_cv/bsadf_cv boundary datestamp/ autoplot need, not just the three scalar critical values summary() uses.

Usage

radf_sbz_cv(
  data,
  minw = NULL,
  nboot = 499L,
  kernel = c("gaussian", "uniform"),
  h = NULL,
  seed = NULL
)

Arguments

data: A univariate or multivariate numeric time series object, a numeric vector or matrix, or a data.frame. A column may have leading and/or trailing NA values (an uneven/unbalanced panel where series enter or exit the sample at different times) – those periods are filled with NA in badf/bsadf and excluded from that series' adf/sadf/ gsadf. Interior NA values (a gap in the middle of a series) are not supported. When any series is padded this way, the panel statistic (bsadf_panel/gsadf_panel) is not available and is returned as NA, with a warning.
minw: A positive integer. The minimum window size (default = \((0.01 + 1.8/\sqrt(T))T\), 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. Default: leave-one-out cross-validation over the paper's own search range.
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 reproducible across the parallel and non-parallel option when the same seed is used.

Value

An object of class radf_cv/sbz_cv/wb_cv: a list with critical values adf_cv, sadf_cv, gsadf_cv (one row per series) and badf_cv, bsadf_cv (one array per series, one row per recursion point).

Status

References

Harvey, D. I., Leybourne, S. J., & Zu, Y. (2019). Testing explosive bubbles with time-varying volatility. Econometric Reviews, 38(10), 1131-1151.