| kpss.test {tseries} | R Documentation |
Computes the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for the
null hypothesis that x is level or trend stationary.
kpss.test(x, null = c("Level", "Trend"), lshort = TRUE)
x |
a numeric vector or univariate time series. |
null |
indicates the null hypothesis and must be one of
"Level" (default) or "Trend". You can specify just
the initial letter. |
lshort |
a logical indicating whether the short or long version of the truncation lag parameter is used. |
To estimate sigma^2 the Newey-West estimator is used.
If lshort is TRUE, then the truncation lag parameter is
set to trunc(3*sqrt(n)/13), otherwise
trunc(10*sqrt(n)/14) is used. The p-values are interpolated
from Table 1 of Kwiatkowski et al. (1992). If the computed statistic
is outside the table of critical values, then a warning message is
generated.
Missing values are not handled.
A list with class "htest" containing the following components:
statistic |
the value of the test statistic. |
parameter |
the truncation lag parameter. |
p.value |
the p-value of the test. |
method |
a character string indicating what type of test was performed. |
data.name |
a character string giving the name of the data. |
A. Trapletti
D. Kwiatkowski, P. C. B. Phillips, P. Schmidt, and Y. Shin (1992): Testing the Null Hypothesis of Stationarity against the Alternative of a Unit Root. Journal of Econometrics 54, 159–178.
x <- rnorm(1000) # is level stationary kpss.test(x) y <- cumsum(x) # has unit root kpss.test(y) x <- 0.3*(1:1000)+rnorm(1000) # is trend stationary kpss.test(x, null = "Trend")