R help - Mar 2005 - The null hypothesis in kpss test (kpss.test())

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? 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)

So this says we can have more confidence in saying r
is _not_ stationary? Should I worry about the
warnings?

Thanks very much.
Weiguang

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
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>

Understood!
And thanks!

Weiguang

 --- Achim Zeileis <Achim.Zeileis at wu-wien.ac.at>
wrote: 
> 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...