radf_sb_cv {exuber}

Panel Sieve Bootstrap Critical Values

Description

radf_sb_cv computes critical values for the panel recursive unit root test using the sieve bootstrap procedure outlined in Pavlidis et al. (2016). radf_sb_distr computes the distribution.

Usage

radf_sb_cv(data, minw = NULL, lag = 0L, nboot = 500L, seed = NULL)

radf_sb_distr(data, minw = NULL, lag = 0L, nboot = 500L, seed = NULL)

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).

lag

A non-negative integer. The lag length of the Augmented Dickey-Fuller regression (default = 0L).

nboot

A positive integer. Number of bootstraps (default = 500L).

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.

Value

For radf_sb_cv A list A list that contains the critical values for the panel BSADF and panel GSADF test statistics. For radf_wb_dist a numeric vector that contains the distribution of the panel GSADF statistic.

References

Pavlidis, E., Yusupova, A., Paya, I., Peel, D., Martínez-García, E., Mack, A., & Grossman, V. (2016). Episodes of exuberance in housing markets: In search of the smoking gun. The Journal of Real Estate Finance and Economics, 53(4), 419-449.

See Also

radf_mc_cv for Monte Carlo critical values and radf_wb_cv for wild Bootstrap critical values

Examples

rsim_data <- radf(sim_data, lag = 1)

# Critical vales should have the same lag length with \code{radf()}
sb <- radf_sb_cv(sim_data, lag = 1)

tidy(sb)

summary(rsim_data, cv = sb)

autoplot(rsim_data, cv = sb)

# Simulate distribution
sdist <- radf_sb_distr(sim_data, lag = 1, nboot = 1000)

autoplot(sdist)