The R package bootUR
implements several bootstrap tests for unit roots, both for single time
series and for (potentially) large systems of time series.
The package can be installed from CRAN using
The development version of the bootUR package can be
installed from GitHub using
When installing from GitHub, in order to build the package from source, you need to have the appropriate R development tools installed (Rtools on Windows, or these tools on Mac).
If you want the vignette to appear in your package when installing from GitHub, use
# install.packages("devtools")
devtools::install_github("smeekes/bootUR", build_vignettes = TRUE, dependencies = TRUE)instead. As building the vignette may take a bit of time (all bootstrap code below is run), package installation will be slower this way.
bootUR provides a few simple tools to check if your data
are suitable to be bootstrapped.
The bootstrap tests in bootUR do not work with missing
data, although multivariate time series with different start and end
dates (unbalanced panels) are allowed. bootUR provides a
simple function to check if your data contain missing values. We will
illustrate this on the MacroTS dataset of macroeconomic
time series that comes with the package.
data("MacroTS")
check_missing_insample_values(MacroTS)
#> GDP_BE GDP_DE GDP_FR GDP_NL GDP_UK CONS_BE CONS_DE CONS_FR CONS_NL CONS_UK
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
#> HICP_BE HICP_DE HICP_FR HICP_NL HICP_UK UR_BE UR_DE UR_FR UR_NL UR_UK
#> FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSEIf your time series have different starting and end points (and thus
some series contain NAs at the beginning and/or end of your
sample, the resampling-based moving block bootstrap (MBB) and sieve
bootstrap (SB) cannot be used. bootUR lets you check the
start and end points as follows:
sample_check <- find_nonmissing_subsample(MacroTS)
# Provides the number of the first and last non-missing observation for each series:
sample_check$range
#> GDP_BE GDP_DE GDP_FR GDP_NL GDP_UK CONS_BE CONS_DE CONS_FR CONS_NL
#> first 1 1 1 5 1 1 1 1 5
#> last 100 100 100 100 100 100 100 100 100
#> CONS_UK HICP_BE HICP_DE HICP_FR HICP_NL HICP_UK UR_BE UR_DE UR_FR UR_NL
#> first 1 9 9 9 9 9 1 1 1 1
#> last 100 100 100 100 100 100 100 100 100 100
#> UR_UK
#> first 1
#> last 100
# Gives TRUE if the time series all start and end at the same observation:
sample_check$all_equal
#> [1] FALSEIf you have ggplot2 installed, you can also plot the
missing data patterns in your series to get a quick overview. You may
need to manipulate some arguments to get the plot properly sized
(therefore it is not run here automatically).
As the standard test for unit roots, bootUR also has an
implementation of the standard, non-bootstrap, augmented Dickey-Fuller
(ADF) test (though its use is not recommended if sample sizes are
small). For this purpose the adf() function can be used.
The function allows to set many options. First, one can choose between
the classical single-step procedure (two_step = FALSE), in
which deterministic components are directly included in the test
regression, and the more flexible and modern two-step procedure
(two_step = TRUE) where deterministic components are first
removed before applying the unit root test to detrended data. For the
standard ADF test, the two specifications generally yield nearly
identical results.
Lag selection
Lag length selection is done automatically in the ADF regression; the
default is by the modified Akaike information criterion (MAIC) proposed
by Ng and Perron (2001) with the correction of Perron and Qu (2008).
Other options include the regular Akaike information criterion (AIC), as
well as the Bayesian information criterion and its modified variant. In
addition, the rescaling suggested by Cavaliere et al. (2015) is
implemented to improve the power of the test under heteroskedasticity;
this can be turned off by setting criterion_scale = FALSE.
To overwrite data-driven lag length selection with a pre-specified lag
length, simply set both the minimum min_lag and maximum lag
length max_lag for the selection algorithm equal to the
desired lag length.
Implementation
We illustrate the ADF test here on Dutch GDP for the two-step specification, including a linear trend in the specification.
GDP_NL <- MacroTS[, 4]
adf(GDP_NL, deterministics = "trend")
#>
#> Two-step ADF test (with trend) on a single time series
#>
#> data: GDP_NL
#> null hypothesis: Series has a unit root
#> alternative hypothesis: Series is stationary
#>
#> estimate largest root statistic p-value
#> GDP_NL 0.9471 -2.515 0.3202To perform a bootstrap version of the ADF unit root test on a single
time series, use the boot_adf() function. The function
allows to set many options, including the bootstrap method used (option
bootstrap), the deterministic components included (option
deterministics) and the type of detrending used (option
detrend). While detrend = "OLS" gives the
standard ADF test, detrend = "QD" provides the powerful
DF-GLS test of Elliott, Rothenberg and Stock (1996). Here we use the
terminology Quasi-Differencing (QD) rather than GLS as this conveys the
meaning less ambiguously and is the same terminology used by Smeekes and
Taylor (2012) and Smeekes (2013). In all cases, two-step detrending is
used.
Implementation
We illustrate the bootstrap ADF test here on Dutch GDP, with the
sieve bootstrap (bootstrap = SB) as in the specification
used by Palm, Smeekes and Urbain (2008) and Smeekes (2013). To get the
well-known test proposed by Paparoditis and Politis (2003), set
bootstrap = "MBB". We set only 399 bootstrap replications
(B = 399) to prevent the code from running too long. We add
an intercept and a trend (deterministics = "trend") and OLS
detrending. The console gives you live updates on the bootstrap
progress. To turn these off, set show_progress = FALSE. The
bootstrap loop can be run in parallel by setting
do_parallel = TRUE (the default).
As random number generation is required to draw bootstrap samples, we first set the seed of the random number generator to obtain replicable results.
set.seed(155776)
boot_adf(GDP_NL, B = 399, bootstrap = "SB", deterministics = "trend",
detrend = "OLS", do_parallel = FALSE)
#> Progress: |------------------|
#> ********************
#>
#> SB bootstrap OLS test (with intercept and trend) on a single time
#> series
#>
#> data: GDP_NL
#> null hypothesis: Series has a unit root
#> alternative hypothesis: Series is stationary
#>
#> estimate largest root statistic p-value
#> GDP_NL 0.9471 -2.515 0.1454Use boot_union() for a test based on the union of
rejections of 4 tests with different number of deterministic components
and different type of detrending (Smeekes and Taylor, 2012). The
advantage of the union test is that you don’t have to specify these
(rather influential) specification tests. This makes the union test a
safe option for quick or automatic unit root testing where careful
manual specification setup is not viable. Here we illustrate it with the
sieve wild bootstrap as proposed by Smeekes and Taylor (2012).
boot_union(GDP_NL, B = 399, bootstrap = "SWB", do_parallel = FALSE)
#> Progress: |------------------|
#> ********************
#>
#> SWB bootstrap union test on a single time series
#>
#> data: GDP_NL
#> null hypothesis: Series has a unit root
#> alternative hypothesis: Series is stationary
#>
#> estimate largest root statistic p-value
#> GDP_NL NA -0.7115 0.614The function boot_panel performs a test on a
multivariate (panel) time series by testing the null hypothesis that all
series have a unit root. A rejection is typically interpreted as
evidence that a ‘significant proportion’ of the series is stationary,
although how large that proportion is - or which series are stationary -
is not given by the test. The test is based on averaging the individual
test statistics, also called the Group-Mean (GM) test in Palm, Smeekes
and Urbain (2011).
Palm, Smeekes and Urbain (2011) introduced this test with the moving
block bootstrap (bootstrap = "MBB"). However, this
resampling-based method cannot handle unbalancedness, and will therefore
give an error when applied to MacroTS:
boot_panel(MacroTS, bootstrap = "MBB", B = 399, do_parallel = FALSE)
#> Error in check_inputs(data = data, boot_sqt_test = boot_sqt_test, boot_ur_test = boot_ur_test, : Resampling-based bootstraps MBB and SB cannot handle unbalanced series.Therefore, you should switch to one of the wild bootstrap methods. Here we illustrate it with the dependent wild bootstrap (DWB) of Shao (2010) and Rho and Shao (2019).
By default the union test is used for each series
(union = TRUE), if this is set to FALSE the
deterministic components and detrending methods can be specified as in
the univariate Dickey-Fuller test.
Although the sieve bootstrap method "SB" and
"SWB" can be used (historically they have been popular
among practitioners), Smeekes and Urbain (2014b) show that these are not
suited to capture general forms of dependence across units. The code
will give a warning to recommend using a different bootstrap method.
boot_panel(MacroTS, bootstrap = "DWB", B = 399, do_parallel = FALSE)
#> Progress: |------------------|
#> ********************
#>
#> Panel DWB bootstrap group-mean union test
#>
#> data: MacroTS
#> null hypothesis: All series have a unit root
#> alternative hypothesis: Some series are stationary
#>
#> estimate largest root statistic p-value
#> MacroTS NA -0.8621 0.1103To perform individual ADF tests on multiple time series
simultaneously, the function boot_ur() can still be used.
As the bootstrap is performed for all series simultaneously,
resampling-based bootstrap methods "MBB" and
"SB" cannot be used directly in case of unbalanced panels.
If they are used anyway, the function will revert to splitting the
bootstrap up and performing it individually per time series. In this
case a warning is given to alert the user.
ADFtests_out <- boot_ur(MacroTS[, 1:5], bootstrap = "MBB", B = 399, union = FALSE,
deterministics = "trend", detrend = "OLS", do_parallel = FALSE)
#> Warning in check_inputs(data = data, boot_sqt_test = boot_sqt_test,
#> boot_ur_test = boot_ur_test, : Missing values cause resampling bootstrap to be
#> executed for each time series individually.
#> Progress: |------------------|
#> ********************
print(ADFtests_out)
#>
#> MBB bootstrap ADF test (with intercept and trend) on each individual
#> series (no multiple testing correction)
#>
#> data: MacroTS[, 1:5]
#> null hypothesis: Series has a unit root
#> alternative hypothesis: Series is stationary
#>
#> Tests performed on each series:
#> estimate largest root statistic p-value
#> GDP_BE 0.9304 -2.792 0.1955
#> GDP_DE 0.8911 -2.774 0.1003
#> GDP_FR 0.9655 -2.049 0.5113
#> GDP_NL 0.9471 -2.515 0.1930
#> GDP_UK 0.9600 -2.449 0.2882Note that boot_ur (intentionally) does not provide a
correction for multiple testing; of course, if we perform each test with
a significance level of 5%, the probability of making a mistake in all
these tests becomes (much, if N is large) more than 5%. To
explicitly account for multiple testing, use the functions
boot_sqt() or boot_fdr().
The function boot_sqt() performs the Bootstrap
Sequential Quantile Test (BSQT) proposed by Smeekes (2015). Here we
split the series in groups which are consecutively tested for unit
roots, starting with the group most likely to be stationary (having the
smallest ADF statistics). If the unit root hypothesis cannot be rejected
for the first group, the algorithm stops; if there is a rejection, the
second group is tested, and so on.
Most options are the same as for boot_panel. The
parameter SQT_level controls the significance level of the
individual tests performed in the sequence, with a default value of
0.05. The other important new parameter to set here is the group sizes.
These can either be set in units, or in fractions of the total number of
series (i.e. quantiles, hence the name) via the parameter
steps. If we have N time series, setting
steps = 0:N means each unit should be tested sequentially.
To split the series in four equally sized groups (regardless of many
series there are), use steps = 0:4 / 4. By convention and
in accordance with notation in Smeekes (2015), the first entry of the
vector should be equal to zero, while the second entry indicates the end
of the first group, and so on. However, if the initial zero is
accidentally omitted, it is automatically added by the function.
Similarly, if the final value is not equal to 1 (in case of
quantiles) or N to end the last group, this is added by the
function.
Testing individual series consecutively is easiest for
interpretation, but is only meaningful if N is small. In
this case the method is equivalent to the bootstrap StepM method of
Romano and Wolf (2005), which controls the familywise error rate, that
is the probability of making at least one false rejection. This can get
very conservative if N is large, and you would typically
end up not rejecting any null hypothesis. The method is illustrated with
the autoregressive wild bootstrap of Smeekes and Urbain (2014a) and
Friedrich, Smeekes and Urbain (2020).
N <- ncol(MacroTS)
# Test each unit sequentially
boot_sqt(MacroTS, steps = 0:N, bootstrap = "AWB", B = 399, do_parallel = FALSE)
#> Progress: |------------------|
#> ********************
#>
#> AWB bootstrap sequential quantile union test
#>
#> data: MacroTS
#> null hypothesis: Series has a unit root
#> alternative hypothesis: Series is stationary
#>
#> Sequence of tests:
#> H0: # I(0) H1: # I(0) tstat p-value
#> Step 1 0 1 -1.661 0.02256
#> Step 2 1 2 -1.413 0.12281
# Split in four equally sized groups (motivated by the 4 series per country)
boot_sqt(MacroTS, steps = 0:4 / 4, bootstrap = "AWB", B = 399, do_parallel = FALSE)
#> Progress: |------------------|
#> ********************
#>
#> AWB bootstrap sequential quantile union test
#>
#> data: MacroTS
#> null hypothesis: Series has a unit root
#> alternative hypothesis: Series is stationary
#>
#> Sequence of tests:
#> H0: # I(0) H1: # I(0) tstat p-value
#> Step 1 0 5 -1.052 0.05013The function boot_fdr() controls for multiple testing by
controlling the false discovery rate (FDR), which is defined as the
expected proportion of false rejections relative to the total number of
rejections. This scales with the total number of tests, making it more
suitable for large N than the familywise error rate.
The bootstrap method for controlling FDR was introduced by Romano,
Shaikh and Wolf (2008), who showed that, unlike the classical way to
control FDR, the bootstrap is appropriate under general forms of
dependence between series. Moon and Perron (2012) applied this method to
unit root testing; it is essentially their method which is implemented
in boot_fdr() though again with the option to change the
bootstrap used (their suggestion was MBB). The arguments to be set are
the same as for the other multivariate unit root tests, with the
exception of FDR_level wihch controls the FDR level. As
BSQT, the method only report those tests until no rejection occurs.
We illustrate it here with the final available bootstrap method, the block wild bootstrap of Shao (2011) and Smeekes and Urbain (2014a).
boot_fdr(MacroTS[, 1:10], FDR_level = 0.1, bootstrap = "BWB", B = 399, do_parallel = FALSE)
#> Progress: |------------------|
#> ********************
#>
#> BWB bootstrap union test with false discovery rate control
#>
#> data: MacroTS[, 1:10]
#> null hypothesis: Series has a unit root
#> alternative hypothesis: Series is stationary
#>
#> Sequence of tests:
#> tstat critical value
#> GDP_DE -1.077 -1.581Generally the unit root tests above would only be used as a single
step in a larger algorithm to determine the orders of integration of the
time series in the dataset. In particular, many economic datasets
contain variables that have order of integration 2, and would so need to
be differenced twice to eliminate all trends. A standard unit root test
cannot determine this however. For this purpose, we add the function
order_integration() which performs a sequence of unit root
tests to determine the orders of each time series.
Starting from a maximum order (by default equal to 2), it differences the data time until there can be at most one unit root. If the test is not rejected for a particular series, we know this series if of order . The series for which we do reject are integrated once (such that they are differenced times from their original level), and the test is repeated. By doing so until we have classified all series, we obtain a full specification of the orders of all time series.
The function allows us to choose which unit root test we want to use.
Here we take boot_fdr. We don’t only get the orders out,
but also the appropriately differenced data.
out_orders <- order_integration(MacroTS[, 11:15], method = "boot_fdr", B = 399,
do_parallel = FALSE)
#> Progress: |------------------|
#> ********************
#> Progress: |------------------|
#> ********************
# Orders
out_orders$order_int
#> HICP_BE HICP_DE HICP_FR HICP_NL HICP_UK
#> 0 0 1 0 1
# Differenced data
stationary_data <- out_orders$diff_dataTo achieve the differencing, order_integration() uses
the function diff_mult() which is also available as
stand-alone function in the package. Finally, a function is provided to
plot the found orders (not run):