sync_test: Time Series Trend Synchronicity Test
In funtimes: Functions for Time Series Analysis
sync_test R Documentation
Time Series Trend Synchronicity Test
Description
Nonparametric test for synchronicity of parametric trends in multiple time series
\insertCiteLyubchich_Gel_2016_synchronismfuntimes.
The method tests whether N observed time series exhibit the same trend
of some pre-specified smooth parametric form.
Usage
sync_test(
formula,
B = 1000,
Window = NULL,
q = NULL,
j = NULL,
ar.order = NULL,
ar.method = "HVK",
ic = "BIC"
)
Arguments
formula
an object of class "formula",
specifying the form of the common parametric time trend to be tested
in a T by N matrix of time series
(time series in columns). Variable t should be used to specify the form of
the trend, where t is specified within the function as a regular sequence
on the interval (0,1]. See ‘Examples’.
B
number of bootstrap simulations to obtain empirical critical values.
Default is 1000.
Window
scalar or N-vector with lengths of the local windows (factors).
If only one value is set, the same Window is applied to each time series.
An N-vector gives a specific window for each time series.
If Window is not specified, an automatic algorithm for optimal
window selection is applied as a default option (see ‘Details’).
q
scalar from 0 to 1 to define the set of possible windows T*q^j
and to automatically select an optimal window for each time series.
Default is 3/4. This argument is ignored if the Window is set by the user.
j
numeric vector to define the set of possible windows T*q^j
and to automatically select an optimal window for each time series.
Default is c(8:11). This argument is ignored if the Window is set by the user.
ar.order
order of the autoregressive filter when ic = "none",
or the maximal order for IC-based filtering. Default is round(10*log10(T)).
The ar.order can be a scalar or N-vector. If scalar, the same
ar.order is applied to each time series. An N-vector specifies
a separate ar.order for each time series.
ar.method
method of estimating autoregression coefficients.
Default "HVK" delivers robust difference-based estimates by
\insertCiteHall_VanKeilegom_2003;textualfuntimes. Alternatively,
options of ar function can be used, such as "burg",
"ols", "mle", and "yw".
ic
information criterion used to select the order of autoregressive filter (AIC of BIC),
considering models of orders p= 0,1,...,ar.order.
If ic = "none", the AR(p) model with p= ar.order is used,
without order selection.
Details
Arguments Window, j, and q are used to set windows
for the local regression. Current version of the function assumes two options:
(1) user specifies one fixed window for each time series using the argument
Window (if Window is set, j and q are ignored),
and (2) user specifies a set of windows by j and q to apply
this set to each time series and to select an optimal window using a heuristic
m-out-of-n subsampling algorithm \insertCiteBickel_Sakov_2008funtimes.
The option of selecting windows automatically for some of the time series,
while for other time series the window is fixed, is not available yet.
If none of these three arguments is set, default j and q are used.
Values T*q^j are mapped to the largest previous integer, then only
those greater than 2 are used.
See more details in \insertCiteLyubchich_Gel_2016_synchronism;textualfuntimes
and \insertCiteLyubchich_2016_trends;textualfuntimes.
Value
A list of class "htest" containing the following components:
method
name of the method.
data.name
name of the data.
statistic
value of the test statistic.
p.value
p-value of the test.
alternative
alternative hypothesis.
estimate
list with elements common_trend_estimates,
ar_order_used, Window_used, wavk_obs, and
all_considered_windows. The latter is a table with bootstrap and
asymptotic test results for all considered windows, that is, without adaptive
selection of the local window.
Author(s)
Yulia R. Gel, Vyacheslav Lyubchich, Ethan Schaeffer, Xingyu Wang
References
\insertAllCited
See Also
ar, HVK, WAVK,
wavk_test
Examples
#Fix seed for reproducible simulations:
set.seed(1)
# Simulate two autoregressive time series of length n without trend
#(i.e., with zero or constant trend)
# and arrange the series into a matrix:
n <- 200
y1 <- arima.sim(n = n, list(order = c(1, 0, 0), ar = c(0.6)))
y2 <- arima.sim(n = n, list(order = c(1, 0, 0), ar = c(-0.2)))
Y <- cbind(y1, y2)
plot.ts(Y)
#Test H0 of a common linear trend:
## Not run:
sync_test(Y ~ t, B = 500)
## End(Not run)
# Sample output:
## Nonparametric test for synchronism of parametric trends
##
##data: Y
##Test statistic = -0.0028999, p-value = 0.7
##alternative hypothesis: common trend is not of the form Y ~ t.
##sample estimates:
##$common_trend_estimates
## Estimate Std. Error t value Pr(>|t|)
##(Intercept) -0.02472566 0.1014069 -0.2438261 0.8076179
##t 0.04920529 0.1749859 0.2811958 0.7788539
##
##$ar.order_used
## y1 y2
##ar.order 1 1
##
##$Window_used
## y1 y2
##Window 15 8
##
##$all_considered_windows
## Window Statistic p-value Asympt. p-value
## 8 -0.000384583 0.728 0.9967082
## 11 -0.024994408 0.860 0.7886005
## 15 -0.047030164 0.976 0.6138976
## 20 -0.015078579 0.668 0.8714980
##
##$wavk_obs
##[1] 0.05827148 -0.06117136
# Add a time series y3 with a different linear trend and re-apply the test:
y3 <- 1 + 3*((1:n)/n) + arima.sim(n = n, list(order = c(1, 0, 0), ar = c(-0.2)))
Y2 <- cbind(Y, y3)
plot.ts(Y2)
## Not run:
sync_test(Y2 ~ t, B = 500)
## End(Not run)
# Sample output:
## Nonparametric test for synchronism of parametric trends
##
##data: Y2
##Test statistic = 0.48579, p-value < 2.2e-16
##alternative hypothesis: common trend is not of the form Y2 ~ t.
##sample estimates:
##$common_trend_estimates
## Estimate Std. Error t value Pr(>|t|)
##(Intercept) -0.3632963 0.07932649 -4.57976 8.219360e-06
##t 0.7229777 0.13688429 5.28167 3.356552e-07
##
##$ar.order_used
## Y.y1 Y.y2 y3
##ar.order 1 1 0
##
##$Window_used
## Y.y1 Y.y2 y3
##Window 8 11 8
##
##$all_considered_windows
## Window Statistic p-value Asympt. p-value
## 8 0.4930069 0 1.207378e-05
## 11 0.5637067 0 5.620248e-07
## 15 0.6369703 0 1.566057e-08
## 20 0.7431621 0 4.201484e-11
##
##$wavk_obs
##[1] 0.08941797 -0.07985614 0.34672734
#Other hypothesized trend forms can be specified, for example:
## Not run:
sync_test(Y ~ 1) #constant trend
sync_test(Y ~ poly(t, 2)) #quadratic trend
sync_test(Y ~ poly(t, 3)) #cubic trend
## End(Not run)
funtimes documentation built on March 31, 2023, 7:35 p.m.
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| sync_test | R Documentation |
Time Series Trend Synchronicity Test
Description
Nonparametric test for synchronicity of parametric trends in multiple time series
\insertCiteLyubchich_Gel_2016_synchronismfuntimes.
The method tests whether N observed time series exhibit the same trend
of some pre-specified smooth parametric form.
Usage
sync_test(
formula,
B = 1000,
Window = NULL,
q = NULL,
j = NULL,
ar.order = NULL,
ar.method = "HVK",
ic = "BIC"
)
Arguments
formula |
an object of class " |
B |
number of bootstrap simulations to obtain empirical critical values. Default is 1000. |
Window |
scalar or |
q |
scalar from 0 to 1 to define the set of possible windows |
j |
numeric vector to define the set of possible windows |
ar.order |
order of the autoregressive filter when |
ar.method |
method of estimating autoregression coefficients.
Default |
ic |
information criterion used to select the order of autoregressive filter (AIC of BIC),
considering models of orders |
Details
Arguments Window, j, and q are used to set windows
for the local regression. Current version of the function assumes two options:
(1) user specifies one fixed window for each time series using the argument
Window (if Window is set, j and q are ignored),
and (2) user specifies a set of windows by j and q to apply
this set to each time series and to select an optimal window using a heuristic
m-out-of-n subsampling algorithm \insertCiteBickel_Sakov_2008funtimes.
The option of selecting windows automatically for some of the time series,
while for other time series the window is fixed, is not available yet.
If none of these three arguments is set, default j and q are used.
Values T*q^j are mapped to the largest previous integer, then only
those greater than 2 are used.
See more details in \insertCiteLyubchich_Gel_2016_synchronism;textualfuntimes and \insertCiteLyubchich_2016_trends;textualfuntimes.
Value
A list of class "htest" containing the following components:
method |
name of the method. |
data.name |
name of the data. |
statistic |
value of the test statistic. |
p.value |
|
alternative |
alternative hypothesis. |
estimate |
list with elements |
Author(s)
Yulia R. Gel, Vyacheslav Lyubchich, Ethan Schaeffer, Xingyu Wang
References
\insertAllCitedSee Also
ar, HVK, WAVK,
wavk_test
Examples
#Fix seed for reproducible simulations:
set.seed(1)
# Simulate two autoregressive time series of length n without trend
#(i.e., with zero or constant trend)
# and arrange the series into a matrix:
n <- 200
y1 <- arima.sim(n = n, list(order = c(1, 0, 0), ar = c(0.6)))
y2 <- arima.sim(n = n, list(order = c(1, 0, 0), ar = c(-0.2)))
Y <- cbind(y1, y2)
plot.ts(Y)
#Test H0 of a common linear trend:
## Not run:
sync_test(Y ~ t, B = 500)
## End(Not run)
# Sample output:
## Nonparametric test for synchronism of parametric trends
##
##data: Y
##Test statistic = -0.0028999, p-value = 0.7
##alternative hypothesis: common trend is not of the form Y ~ t.
##sample estimates:
##$common_trend_estimates
## Estimate Std. Error t value Pr(>|t|)
##(Intercept) -0.02472566 0.1014069 -0.2438261 0.8076179
##t 0.04920529 0.1749859 0.2811958 0.7788539
##
##$ar.order_used
## y1 y2
##ar.order 1 1
##
##$Window_used
## y1 y2
##Window 15 8
##
##$all_considered_windows
## Window Statistic p-value Asympt. p-value
## 8 -0.000384583 0.728 0.9967082
## 11 -0.024994408 0.860 0.7886005
## 15 -0.047030164 0.976 0.6138976
## 20 -0.015078579 0.668 0.8714980
##
##$wavk_obs
##[1] 0.05827148 -0.06117136
# Add a time series y3 with a different linear trend and re-apply the test:
y3 <- 1 + 3*((1:n)/n) + arima.sim(n = n, list(order = c(1, 0, 0), ar = c(-0.2)))
Y2 <- cbind(Y, y3)
plot.ts(Y2)
## Not run:
sync_test(Y2 ~ t, B = 500)
## End(Not run)
# Sample output:
## Nonparametric test for synchronism of parametric trends
##
##data: Y2
##Test statistic = 0.48579, p-value < 2.2e-16
##alternative hypothesis: common trend is not of the form Y2 ~ t.
##sample estimates:
##$common_trend_estimates
## Estimate Std. Error t value Pr(>|t|)
##(Intercept) -0.3632963 0.07932649 -4.57976 8.219360e-06
##t 0.7229777 0.13688429 5.28167 3.356552e-07
##
##$ar.order_used
## Y.y1 Y.y2 y3
##ar.order 1 1 0
##
##$Window_used
## Y.y1 Y.y2 y3
##Window 8 11 8
##
##$all_considered_windows
## Window Statistic p-value Asympt. p-value
## 8 0.4930069 0 1.207378e-05
## 11 0.5637067 0 5.620248e-07
## 15 0.6369703 0 1.566057e-08
## 20 0.7431621 0 4.201484e-11
##
##$wavk_obs
##[1] 0.08941797 -0.07985614 0.34672734
#Other hypothesized trend forms can be specified, for example:
## Not run:
sync_test(Y ~ 1) #constant trend
sync_test(Y ~ poly(t, 2)) #quadratic trend
sync_test(Y ~ poly(t, 3)) #cubic trend
## End(Not run)
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