Description Usage Arguments Details Value References See Also Examples
View source: R/monitorStationarity.R
This procedure is able to monitor a onedimensional vector for level or
trend stationarity and returns the corresponding break point, if available.
It is based on parameter estimation on a prebreak "calibration" period
at the beginning of the sample that is known or assumed to be free of
structural change and can be specified exactly via the m
argument
(see Details for further information).
1 2 3 
x 
[ 
m 
[ 
trend 
[ 
kernel 
[ 
bandwidth 
[ 
signif.level 
[ 
return.stats 
[ 
return.input 
[ 
check 
[ 
... 
Arguments passed to 
The calibration period can be specified by setting the argument m
to the number of its last observation.
The corresponding fraction of the data's length will be calculated
automatically. Alternatively you can set m
directly to the fitting
fraction value. Attention: The calibration period may become smaller than
intended: The last observation is calculated as floor(m * N)
(with N
= length of x
).
The kernel that is used for calculating the longrun variance can be one of the following:
"ba"
: Bartlett kernel
"pa"
: Parzen kernel
"qs"
: Quadratic Spectral kernel
"tr"
: Truncated kernel
[cointmonitoR
] object with components:
Hsm
[numeric(1)
]value of the test statistic
time
[numeric(1)
]detected time of structural break
p.value
[numeric(1)
]estimated pvalue of the test (between 0.01 and 0.1)
cv
[numeric(1)
]critical value of the test
sig
[numeric(1)
]significance level used for the test
trend
[character(1)
]trend model ("level" or "trend")
name
[character(1)
]name(s) of data
m
[list(2)
]list with components:
$m.frac
[numeric(1)
]: calibration period (fraction)
$m.index
[numeric(1)
]: calibration period (length)
kernel
[character(1)
]kernel function
bandwidth
[list(2)
]$name
[character(1)
]: bandwidth function (name)
$number
[numeric(1)
]: bandwidth
statistics
[numeric
]values of test statistics with the same length as data, but NA
during calibration period (available if return.stats = TRUE
)
input
[numeric
 matrix
 data.frame
]copy of input data (available if return.stats = TRUE
)
Wagner, M. and D. Wied (2015): "Monitoring Stationarity and Cointegration," Discussion Paper, DOI:10.2139/ssrn.2624657.
Other cointmonitoR: monitorCointegration
,
plot.cointmonitoR
,
print.cointmonitoR
1 2 3 4 5 6 7 8 9 10 11 12 13  set.seed(1909)
x < rnorm(200)
x2 < c(x[1:100], cumsum(x[101:200]) / 2)
# Specify the calibration period
# as fraction of the total length of x:
monitorStationarity(x, m = 0.25)
monitorStationarity(x2, m = 0.465)
# Specify the calibration period
# by setting its last observation exactly:
monitorStationarity(x, m = 50)
monitorStationarity(x2, m = 93)

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