boot_df {bootUR}  R Documentation 
This function performs a standard augmented DickeyFuller bootstrap unit root test on a single time series.
boot_df(y, level = 0.05, boot = "AWB", B = 1999, l = NULL, ar_AWB = NULL, p_min = 0, p_max = NULL, ic = "MAIC", dc = 1, detr = "OLS", ic_scale = TRUE, verbose = FALSE, show_progress = FALSE, do_parallel = FALSE, nc = NULL)
y 
A Tdimensional vector to be tested for unit roots. Data may also be in a time series format (e.g. 
level 
Desired significance level of the unit root test. Default is 0.05. 
boot 
String for bootstrap method to be used. Options are

B 
Number of bootstrap replications. Default is 1999. 
l 
Desired 'block length' in the bootstrap. For the MBB, BWB and DWB boostrap, this is a genuine block length. For the AWB boostrap, the block length is transformed into an autoregressive parameter via the formula 0.01^(1/l) as in Smeekes and Urbain (2014a); this can be overwritten by setting 
ar_AWB 
Autoregressive parameter used in the AWB bootstrap method ( 
p_min 
Minimum lag length in the augmented DickeyFuller regression. Default is 0. 
p_max 
Maximum lag length in the augmented DickeyFuller regression. Default uses the sample sizebased rule 12(T/100)^{1/4}. 
ic 
String for information criterion used to select the lag length in the augmented DickeyFuller regression. Options are: 
dc 
Numeric vector indicating the deterministic specification. Only relevant if
If 
detr 
String vector indicating the type of detrending to be performed. Only relevant if 
ic_scale 
Logical indicator whether or not to use the rescaled information criteria of Cavaliere et al. (2015) ( 
verbose 
Logical indicator whether or not information on the outcome of the unit root test needs to be printed to the console. Default is 
show_progress 
Logical indicator whether a bootstrap progress update should be printed to the console. Default is FALSE. 
do_parallel 
Logical indicator whether bootstrap loop should be executed in parallel. Parallel computing is only available if OpenMP can be used, if not this option is ignored. Default is FALSE. 
nc 
The number of cores to be used in the parallel loops. Default is to use all but one. 
The options encompass many test proposed in the literature. dc = "OLS"
gives the standard augmented DickeyFuller test, while dc = "QD"
provides the DFGLS test of Elliott, Rothenberg and Stock (1996). The bootstrap algorithm is always based on a residual bootstrap (under the alternative) to obtain residuals rather than a differencebased bootstrap (under the null), see e.g. Palm, Smeekes and Urbain (2008).
Lag length selection is done automatically in the ADF regression with the specified information criterion. If one of the modified criteria of Ng and Perron (2001) is used, the correction of Perron and Qu (2008) is applied. For very short time series (fewer than 50 time points) the maximum lag length is adjusted downward to avoid potential multicollinearity issues in the bootstrap. To overwrite datadriven lag length selection with a prespecified lag length, simply set both the minimum 'p_min' and maximum lag length 'p_max' for the selection algorithm equal to the desired lag length.
Values of the DickeyFuller test statistics and corresponding bootstrap pvalues.
Error: Multiple time series not allowed. Switch to a multivariate method such as iADFtest, or change argument y to a univariate time series.
The function is a simple wrapper around iADFtest
to facilitate use for single time series. It does not support multiple time series, as iADFtest
is specifically suited for that.
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# boot_df on GDP_BE GDP_BE_df < boot_df(MacroTS[, 1], B = 399, dc = 2, detr = "OLS", verbose = TRUE)