From: <markleeds_at_verizon.net>

Date: Thu, 10 Jan 2008 15:05:34 -0600 (CST)

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Thu 10 Jan 2008 - 21:09:47 GMT

Date: Thu, 10 Jan 2008 15:05:34 -0600 (CST)

just to give you some short background

in term of adf:

generally speaking to

decide on whether there is a unit root using adf, the lagged residuals need to
be not serially correlated under the null
hypothesis. so, when you choose a lag

basically you are deciding how many lags of
x do i use in my null model so that there
is no serial correlation in the residuals.

if you put too few lags in, then you end up
with correlated residuals and the test

is wrong. if you put too many in, you

lose power ( the ability to reject the null )
and that's not good either.

i'm not sure if all of the above applied
to KPSS because i forget the detials of

how it's done but it's probably related.

to get some background in time series,

you should read hamilton if you're brave
or enders if you just want to get a quick
( pretty non mathematical ) idea but great
intution. good luck. i replied privately
because others may say stuff more useful
so why not see that also. plus

i didn't answer your question.

mark

>Hi R users!

*>
**>I've come across using kpss tests for time series analysis and i have a question that troubles me since i don't have much experience with time series and the mathematical part underlining it.
**>
**>x<-c(253, 252, 275, 275, 272, 254, 272, 252, 249, 300, 244,
**>258, 255, 285, 301, 278, 279, 304, 275, 276, 313, 292, 302,
**>322, 281, 298, 305, 295, 286, 327, 286, 270, 289, 293, 287,
**>267, 267, 288, 304, 273, 264, 254, 263, 265, 278)
**>x <- ts(x, frequency = 12)
**>library (urca)
**>library (uroot)
**>library (tseries)
**>
**>Now, doing an ur.kpss test (mu, lag=3) i cannot reject the null hypothesis of level stationarity.
**>Doing a kpss.test (mu, lag=1 by default ) the p value becomes smaller than 0.05 thus rejecting the null of stationarity. Same with KPSS.test (lag=1)
**>
**>So, as i have noticed that increasing the number of lags on each of the tests rejecting the null becomes harder and harder. I saw that books always cite use of Bartlett window but the lags determination is left to the analyst. My question: which is the "proper" number of lags, so i don't make any false statements on the data?
**>
**>Thank you and have a great day!
**>
**>
**>
**>---------------------------------
**>
**> [[alternative HTML version deleted]]
**>
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*

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https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. Received on Thu 10 Jan 2008 - 21:09:47 GMT

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