# kpss.test

From tseries v0.9-19
by Kurt Hornik

##### 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: 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.

##### 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

##### 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 0.9-19, License: GPL (see file COPYING)*

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