Description Usage Arguments Details Value Author(s) References See Also Examples
This function performs a family of CUSUM tests for a change-in-mean that are robust under long memory. They apply non-parametric kernel-based fixed-b and fixed-m long-run variance estimators in the denominator of the test statistics. The function returns the test statistic as well as critical values.
1 | CUSUMfixed(x, d, procedure, bandw, tau = 0.15)
|
x |
the univariate numeric vector to be investigated. Missing values are not allowed. |
d |
integer that specifies the long-memory parameter. |
procedure |
string that specifies whether the CUSUM fixed-b or fixed-m type A or type B tests are used. It can be chosen between
|
bandw |
integer that determines the bandwidth used for estimation of the long-run variance. For the fixed-b tests |
tau |
integer that defines the search area, which is |
Note that the critical values are generated for tau=0.15
using the Bartlett kernel for the fixed-b tests or averaging the first m periodogram
ordinates (which corresponds to the Daniell kernel) for the fixed-m tests.
Returns a numeric vector containing the test statistic and the corresponding critical values of the test.
Kai Wenger
Wenger, K. and Leschinski, C. (2019): Change-in-mean tests in long-memory time series: a review of recent developments. AStA Advances in Statistical Analysis, 103:2, pp. 237-256.
Hualde, J. and Iacone, F. (2017): Fixed bandwidth asymptotics for the studentized mean of fractionally integrated processes. Economics Letters, 150, pp. 39-43.
Andrews, D. W. K. (1993): Tests for Parameter Instability and Structural Change With Unknown Change Point. Econometrica, 61, pp. 821-856.
CUSUMLM
, CUSUM_simple
, fixbsupw
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 | # set model parameters
T <- 500
d <- 0.2
set.seed(410)
# generate a fractionally integrated (long-memory) time series
tseries <- fracdiff::fracdiff.sim(n=T, d=d)$series
# generate a fractionally integrated (long-memory) time series
# with a change in mean in the middle of the series
changep <- c(rep(0,T/2), rep(1,T/2))
tseries2 <- tseries+changep
# estimate the long-memory parameter of both series via local
# Whittle approach. The bandwidth to estimate d is chosen
# as T^0.65, which is usual in literature
d_est <- LongMemoryTS::local.W(tseries, m=floor(1+T^0.65))$d
d_est2 <- LongMemoryTS::local.W(tseries2, m=floor(1+T^0.65))$d
# perform the different types of the test on both time series
CUSUMfixed(tseries, d=d_est, procedure="CUSUMfixedb_typeA", bandw=0.1)
CUSUMfixed(tseries, d=d_est, procedure="CUSUMfixedb_typeB", bandw=0.1)
CUSUMfixed(tseries, d=d_est, procedure="CUSUMfixedm_typeA", bandw=10)
CUSUMfixed(tseries, d=d_est, procedure="CUSUMfixedm_typeB", bandw=10)
CUSUMfixed(tseries2, d=d_est2, procedure="CUSUMfixedb_typeA", bandw=0.1)
CUSUMfixed(tseries2, d=d_est2, procedure="CUSUMfixedb_typeB", bandw=0.1)
CUSUMfixed(tseries2, d=d_est2, procedure="CUSUMfixedm_typeA", bandw=10)
CUSUMfixed(tseries2, d=d_est2, procedure="CUSUMfixedm_typeB", bandw=10)
# For the series with no change in mean all tests do not reject
# the null hypothesis of a constant mean across time at
# any reasonable significance level.
# For the series with a change in mean all tests reject the
# null hypothesis at a 1% significance level.
|
90% 95% 99% Teststatistic
1.075 1.400 1.426 0.880
90% 95% 99% Teststatistic
1.720 1.993 2.593 0.985
90% 95% 99% Teststatistic
1.294 1.415 1.614 0.830
90% 95% 99% Teststatistic
1.562 1.785 2.247 0.883
90% 95% 99% Teststatistic
1.194 1.378 1.420 1.555
90% 95% 99% Teststatistic
2.346 2.748 3.612 4.613
90% 95% 99% Teststatistic
1.549 1.664 1.844 2.033
90% 95% 99% Teststatistic
2.221 2.565 3.248 4.133
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