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exuber

Recursive Augmented Dickey-Fuller

Panel Sieve Bootstrap Critical Values

Also: radf_sb_distr

radf_sb_cv 
radf_sb_cv(
  data,
  minw = NULL,
  lag = 0L,
  nboot = 500L,
  type = c("fixed", "aic", "bic"),
  max_lag = 8L,
  seed = NULL
)

radf_sb_distr(
  data,
  minw = NULL,
  lag = 0L,
  nboot = 500L,
  type = c("fixed", "aic", "bic"),
  max_lag = 8L,
  seed = NULL
)

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

Arguments

data A univariate or multivariate numeric time series object, a numeric vector or matrix, or a data.frame. A column may have leading or trailing NA values, which describes an unbalanced panel in which series enter or exit the sample at different times. Those periods are filled with NA in badf and bsadf and excluded from the adf, sadf and gsadf of that series. Interior NA values (a gap in the middle of a series) are not supported. When any series is padded in this way, the panel statistics (bsadf_panel and gsadf_panel) are not available, and the function returns NA for them with a warning.
minw A positive integer. The minimum window size (default = (0.01+1.8/T)T(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).
type Lag-order selection. "fixed" (default) uses lag as given, as in the single-lag behavior of radf. "aic" and "bic" select the lag automatically for each series with lag_select() (internal), and the function takes the maximum across the panel because the rest of it assumes one common lag order. This is the fix of Pedersen & Schütte (2020) for the size distortion that a fixed lag causes under autocorrelated innovations.
max_lag Maximum lag order to search over when type is "aic" or "bic". It is ignored when type = "fixed".
seed An object specifying if and how the random number generator (rng) should be initialized. It is either NULL or an integer, which is passed to set.seed before the simulation. If you set it, the value is saved as the "seed" attribute of the returned value. The default, NULL, leaves the state of the rng unchanged and returns .Random.seed as the "seed" attribute. Results are reproducible across the parallel and the non-parallel option when you use the same seed.

Value

For radf_sb_cv, a list with the critical values for the panel BSADF and panel GSADF test statistics. For radf_sb_distr, a numeric vector with the distribution of the panel GSADF statistic.

Examples

These examples are copied from the package's own documentation and are run by R CMD check on every release. The printed output (after #>) and the plots were produced by running them against the current package source. 

rsim_data <- radf(sim_data, lag = 1)

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

tidy(sb)
#> # A tibble: 3 × 3
#>   id    sig   gsadf_panel
#>   <fct> <fct>       <dbl>
#> 1 panel 90          0.312
#> 2 panel 95          0.449
#> 3 panel 99          0.777

summary(rsim_data, cv = sb)
#> 
#> ── Summary (minw = 19, lag = 1) ─────────────── Sieve Bootstrap (nboot = 500) ──
#> 
#> panel :
#> # A tibble: 1 × 5
#>   stat        tstat  `90`  `95`  `99`
#>   <fct>       <dbl> <dbl> <dbl> <dbl>
#> 1 gsadf_panel  1.89 0.312 0.449 0.777

autoplot(rsim_data, cv = sb)
Plot from the radf_sb_cv example
# Simulate distribution
sdist <- radf_sb_distr(sim_data, lag = 1, nboot = 1000)

autoplot(sdist)
Plot from the radf_sb_cv example
# Automatic BIC lag selection instead of a fixed lag
sb_bic <- radf_sb_cv(sim_data, type = "bic")

See also

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

Other critical values: radf_common_cv(), radf_mc_cv(), radf_recovery_cv(), radf_sbz_cv(), radf_sign_cv(), radf_sign_dm_cv(), radf_tt_cv(), radf_wb_cv(), radf_wb_ps_cv()

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. 10.1007/s11146-015-9531-2

Pedersen, T. Q., & Schütte, E. C. M. (2020). Testing for explosive bubbles in the presence of autocorrelated innovations. Journal of Empirical Finance, 58, 207-225.