exuber (version 0.2.1)

wb_cv: Wild Bootstrap Critical values

Description

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.

Usage

wb_cv(data, minw, nboot = 1000, dist_rad = FALSE)

Arguments

data

A univariate or multivariate numeric ts object, data.frame or matrix. The estimation process cannot handle NA values.

minw

A positive integer. The minimum window size, which defaults to \((0.01 + 1.8/\sqrt(T))*T\).

nboot

A positive integer indicating the number of bootstraps. Default is 1000 repetitions.

dist_rad

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

Value

A list that contains the critical values for ADF, BADF, BSADF and GSADF t-statistics.

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

mc_cv for Monte Carlo critical values and sb_cv for Sieve Bootstrapped critical values

Examples

# NOT RUN {
# Simulate bubble processes
dta <- data.frame("dg1" = sim_dgp1(n = 100), "dgp2" = sim_dgp2(n = 100))

# Default minimum window
wb <- wb_cv(dta)

# Change the minimum window and the number of bootstraps
wb <- wb_cv(dta, nboot = 1500,  minw = 20)

# }