KAUSHIK BHATTACHARJEE a écrit :

> I have some time series .more than 1000 observations each ..I want to test if they contain unit roots...

> I am performing ADF as well as KPSS tests...I am getting contraditory results in 7 out of 10 series....KPSS test is rejecting stationarity where as ADF test is not....

> Which one is more reliable test...any idea/reference?

>

>

any reference? Yes, the paper of KPSS! It is not too technical and in

the empirical part they discuss this issue of contradictory results with

unit roots tests (if I remember well there are five cases from the

Nelson Plosser data were KPSS is different than ADF, so that's a known

issue:-).

I would rather base on a unit root test (btw rather ERS than ADF) as the

KPSS test is rather considered as a "confirmatory test". Base on unit

root tests especially when both tests are rejected, as unit root tests

are known to have low power and rejection is then a pretty "big sign".

But note that their size is not so good neither, while KPSS can have

really big size distortions, try yourself:

library(urca)

simul.cval<-function(ar, n=100){

series<-arima.sim(model=list(ar=ar), n=n)

summary(ur.kpss(series, use.lag=1))@teststat

}

mc<-replicate(500, simul.cval(ar=0.2))

mean(mc> 0.463)

mc<-replicate(500, simul.cval(ar=0.9))

mean(mc> 0.463)

Hope this helps

Mat

> Kaushik Bhattacharjee

>

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