boot_union {bootUR}  R Documentation 
Performs bootstrap unit root test based on the union of rejections of 4 tests with different number of deterministic components and different type of detrending (Harvey, Leybourne and Taylor, 2012; Smeekes and Taylor, 2012).
boot_union(y, level = 0.05, boot = "AWB", B = 1999, l = NULL, ar_AWB = NULL, p_min = 0, p_max = NULL, ic = "MAIC", 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: 
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 union is taken over the combination of tests with intercept only and intercept plus trend, coupled with OLS detrending and QD detrending, as in Harvey, Leybourne and Taylor (2012) and Smeekes an Taylor (2012). 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 regressions 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. 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.
Value of the union test statistic and the 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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Cavaliere, G., Phillips, P.C.B., Smeekes, S., and Taylor, A.M.R. (2015). Lag length selection for unit root tests in the presence of nonstationary volatility. Econometric Reviews, 34(4), 512536.
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Harvey, D.I., Leybourne, S.J., and Taylor, A.M.R. (2012). Testing for unit roots in the presence of uncertainty over both the trend and initial condition. Journal of Econometrics, 169(2), 188195.
Ng, S. and Perron, P. (2001). Lag Length Selection and the Construction of Unit Root Tests with Good Size and Power. Econometrica, 69(6), 15191554,
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Shao, X. (2010). The dependent wild bootstrap. Journal of the American Statistical Association, 105(489), 218235.
Shao, X. (2011). A bootstrapassisted spectral test of white noise under unknown dependence. Journal of Econometrics, 162, 213224.
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Smeekes, S. and Taylor, A.M.R. (2012). Bootstrap union tests for unit roots in the presence of nonstationary volatility. Econometric Theory, 28(2), 422456.
Smeekes, S. and Urbain, J.P. (2014a). A multivariate invariance principle for modified wild bootstrap methods with an application to unit root testing. GSBE Research Memorandum No. RM/14/008, Maastricht University
# boot_union on GDP_BE GDP_BE_df < boot_union(MacroTS[, 1], B = 399, verbose = TRUE)