[R] The null hypothesis in kpss test (kpss.test())

[Achim Zeileis]

As help(kpss.test) tells you: kpss.test() approximates the p values by
interpolation from a simulated table of critical values. As p values
larger than 0.1 are typically regarded to be non-significant and p
values smaller than 0.01 are typically regarded to be highly
significant, the corresponding critical values are only stored for the
range 0.1 to 0.01.

Hence...

On Tue, 8 Mar 2005 12:51:37 -0500 (EST) Weiguang Shi wrote:

> is that 'x' is level or trend stationary. I did this
>  
>   > s<-rnorm(1000)
>   > kpss.test(s)
>  
>         KPSS Test for Level Stationarity
>  
>   data:  s
>   KPSS Level = 0.0429, Truncation lag parameter = 7,
> p-value = 0.1
>  
>   Warning message:
>   p-value greater than printed p-value in:
> kpss.test(s)
> 
> My question is whether p=0.1 is a good number to
> reject N0? 

...stationarity cannot be rejected here (which is not surprising) and...

> On the other hand, I have a series r and did the 
> following:
>   > plot.ts(r)
>   > kpss.test(r)
>  
>         KPSS Test for Level Stationarity
>  
>   data:  r
>   KPSS Level = 3.1955, Truncation lag parameter = 7,
> p-value = 0.01
>  
>   Warning message:
>   p-value smaller than printed p-value in:
> kpss.test(r)

...stationarity is clearly rejected here.

> So this says we can have more confidence in saying r
> is _not_ stationary?

Yes (I guess. I'm not sure about `more confidence'...`more' than what?)
Z

> Should I worry about the warnings?
> 
> Thanks very much.
> Weiguang
> 
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