exuber (version 1.0.2)

radf_wb_cv: Wild Bootstrap Critical Values

Description

radf_wb_cv performs the Harvey et al. (2016) wild bootstrap re-sampling scheme, which is asymptotically robust to non-stationary volatility, to generate critical values for the recursive unit root tests. radf_wb_distr computes the distribution.

Usage

radf_wb_cv(data, minw = NULL, nboot = 500L, dist_rad = FALSE, seed = NULL)
radf_wb_distr(data, minw = NULL, nboot = 500L, dist_rad = FALSE, seed = NULL)

Value

For radf_wb_cv a list that contains the critical values for the ADF, BADF, BSADF and GSADF tests. For radf_wb_distr a list that contains the ADF, SADF and GSADF distributions.

Arguments

data

A univariate or multivariate numeric time series object, a numeric vector or matrix, or a data.frame. The object should not have any NA values.

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).

dist_rad

Logical. If TRUE then the Rademacher distribution will be used.

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.

Details

This approach involves applying a wild bootstrap re-sampling scheme to construct the bootstrap analogue of the Phillips et al. (2015) test which is asymptotically robust to non-stationary volatility.

References

Harvey, D. I., Leybourne, S. J., Sollis, R., & Taylor, A. M. R. (2016). Tests for explosive financial bubbles in the presence of non-stationary volatility. Journal of Empirical Finance, 38(Part B), 548-574.

Phillips, P. C. B., Shi, S., & Yu, J. (2015). Testing for Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500. International Economic Review, 56(4), 1043-1078.

See Also

radf_mc_cv for Monte Carlo critical values and radf_sb_cv for sieve bootstrap critical values.

Examples

# \donttest{
# Default minimum window
wb <- radf_wb_cv(sim_data)

tidy(wb)

# Change the minimum window and the number of bootstraps
wb2 <- radf_wb_cv(sim_data, nboot = 600, minw = 20)

tidy(wb2)

# Simulate distribution
wdist <- radf_wb_distr(sim_data)

autoplot(wdist)
# }