kpss.test

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KPSS Test for Stationarity

Computes the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test for the null hypothesis that x is level or trend stationary.

Keywords
ts
Usage
kpss.test(x, null = c("Level", "Trend"), lshort = TRUE)
Arguments
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.
Details

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.

Value

  • A list with class "htest" containing the following components:
  • statisticthe value of the test statistic.
  • parameterthe truncation lag parameter.
  • p.valuethe p-value of the test.
  • methoda character string indicating what type of test was performed.
  • data.namea character string giving the name of the data.

References

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.

See Also

pp.test

Aliases
  • kpss.test
Examples
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")
Documentation reproduced from package tseries, version 0.9-21, License: GPL (see file COPYING)

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