exuber (version 0.2.1)

sb_cv: Panel Sieve Bootstrap Critical Values

Description

sb_cv computes p-values for the panel recursive unit root test using the sieve bootstrap procedure outlined in Pavlidis et al. (2016)

Usage

sb_cv(data, minw, lag = 0, nboot = 1000)

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 \((0.01 + 1.8/\sqrt(T))*T\).

lag

A non-negative integer. The lag of the Augmented Dickey-Fuller regression.

nboot

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

Value

A list that contains the panel critical values for BSADF and GSADF t-statistics.

References

Pavlidis, E., Yusupova, A., Paya, I., Peel, D., Mart<U+00ED>nez-Garc<U+00ED>a, 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

mc_cv for Monte Carlo critical values and wb_cv for Wild Bootstrapped critical values

Examples

# NOT RUN {
# Simulate bubble processes
set.seed(124)
pdta <- cbind(sim_dgp1(100), sim_dgp1(100), sim_div(100), sim_div(100), sim_div(100))

# Panel critical vales should have the same lag length with the estimation
sb <- sb_cv(pdta, lag = 1)

pdta %>%
  radf(lag = 1) %>%
  summary(cv = sb)

pdta %>%
  radf(lag = 1) %>%
  autoplot(cv = sb)
# }