Description Usage Arguments Details Value Errors and warnings References See Also Examples
Performs a test on a multivariate (panel) time series by testing the null hypothesis that all series have a unit root. The test is based on averaging the individual test statistics, also called the GroupMean (GM) test in Palm, Smeekes and Urbain (2011).
1 2 3 4 
y 
A Tdimensional vector or a (T x N)matrix of N time series with T observations 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 bootstrap, this is a genuine block length. For the AWB bootstrap, 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 ( 
union 
Logical indicator whether or not to use bootstrap union tests ( 
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. 
See iADFtest
for details on the bootstrap algorithm and lag selection.
For the union test (union = TRUE
), the test statistic and pvalue are returned. If union = FALSE
, the test statistics and pvalues are reported per type of deterministic component (dc
) and detrending method (detr
).
Error: Resamplingbased bootstraps MBB and SB cannot handle missing values.
If the time series in y
have different starting and end points (and thus some series contain NA
values at the beginning and/or end of the sample, the resamplingbased moving block bootstrap (MBB) and sieve bootstrap (SB) cannot be used, as they create holes (internal missings) in the bootstrap samples. Switch to another bootstrap method or truncate your sample to eliminate NA
values.
Warning: SB and SWB bootstrap only recommended for iADFtest; see help for details.
Although the sieve bootstrap methods "SB"
and "SWB"
can be used, Smeekes and Urbain (2014b) show that these are not suited to capture general forms of dependence across units, and using them for joint or multiple testing is not valid. This warning thereofre serves to recommend the user to consider a different bootstrap method.
Warning: Deterministic specification in argument dc is ignored, as union test is applied.
The union test calculates the union of all four combinations of deterministic components (intercept or intercept and trend) and detrending methods (OLS or QD). Setting deterministic components manually therefore has no effect.
Warning: Detrending method in argument detr is ignored, as union test is applied.
The union test calculates the union of all four combinations of deterministic components (intercept or intercept and trend) and detrending methods (OLS or QD). Setting detrending methods manually therefore has no effect.
Chang, Y. and Park, J. (2003). A sieve bootstrap for the test of a unit root. Journal of Time Series Analysis, 24(4), 379400.
Cavaliere, G. and Taylor, A.M.R (2009). Heteroskedastic time series with a unit root. Econometric Theory, 25, 1228–1276.
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.
Elliott, G., Rothenberg, T.J., and Stock, J.H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64(4), 813836.
Friedrich, M., Smeekes, S. and Urbain, J.P. (2020). Autoregressive wild bootstrap inference for nonparametric trends. Journal of Econometrics, 214(1), 81109.
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,
Palm, F.C., Smeekes, S. and Urbain, J.P. (2008). Bootstrap unit root tests: Comparison and extensions. Journal of Time Series Analysis, 29(1), 371401.
Palm, F. C., Smeekes, S., and Urbain, J..P. (2011). Crosssectional dependence robust block bootstrap panel unit root tests. Journal of Econometrics, 163(1), 85104.
Paparoditis, E. and Politis, D.N. (2003). Residualbased block bootstrap for unit root testing. Econometrica, 71(3), 813855.
Perron, P. and Qu, Z. (2008). A simple modification to improve the finite sample properties of Ng and Perron's unit root tests. Economic Letters, 94(1), 1219.
Rho, Y. and Shao, X. (2019). Bootstrapassisted unit root testing with piecewise locally stationary errors. Econometric Theory, 35(1), 142166.
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.
Smeekes, S. (2013). Detrending bootstrap unit root tests. Econometric Reviews, 32(8), 869891.
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
Smeekes, S. and Urbain, J.P. (2014b). On the applicability of the sieve bootstrap in time series panels. Oxford Bulletin of Economics and Statistics, 76(1), 139151.
1 2 3 4  # paneltest on GDP_BE and GDP_DE
## Not run: two_series_paneltest < paneltest(MacroTS[, 1:2], boot = "AWB", B = 399,
verbose = TRUE)
## End(Not run)

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