From: Pfaff, Bernhard Dr. <Bernhard_Pfaff_at_fra.invesco.com>

Date: Fri 07 Jul 2006 - 17:26:17 EST

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R-help@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 Received on Fri Jul 07 20:51:56 2006

Date: Fri 07 Jul 2006 - 17:26:17 EST

Hello Sachin,

a sequential testing procedure is described in the useR! book:

@Book{,

title = {Analysis of Integrated and Cointegrated Time Series with R},
author = {B. Pfaff},

publisher = {Springer},

edition = {First},

address = {New York},

year = {2006},

note = {ISBN 0-387-27960-1},

}

Best,

Bernhard

Dr. Bernhard Pfaff

Global Structured Products Group

(Europe)

Invesco Asset Management Deutschland GmbH
Bleichstrasse 60-62

D-60313 Frankfurt am Main

Tel: +49(0)69 29807 230

Fax: +49(0)69 29807 178

Email: bernhard_pfaff@fra.invesco.com

>-----Ursprüngliche Nachricht-----

*>Von: r-help-bounces@stat.math.ethz.ch
**>[mailto:r-help-bounces@stat.math.ethz.ch] Im Auftrag von Sachin J
**>Gesendet: Donnerstag, 6. Juli 2006 21:49
**>An: markleeds@verizon.net
**>Cc: r-help@stat.math.ethz.ch
**>Betreff: Re: [R] KPSS test
**>
**>Hi Mark,
**>
**> Thanx for the help. I will verify my results with PP and DF
**>test. Also as suggested I will take a look at the references
**>pointed out. One small doubt: How do I decide what terms (
**>trend, constant, seasonality ) to include while using these
**>stationarity tests. Any references would be of great help.
**>
**> Thanx,
**> Sachin
**>
**>
**>
**>markleeds@verizon.net wrote:
**> >From:
**>>Date: Thu Jul 06 14:17:25 CDT 2006
**>>To: Sachin J
**>>Subject: Re: [R] KPSS test
**>
**>sachin : i think your interpretations are right given the data
**>but kpss is quite a different test than the usual tests
**>because it assumes that the null is stationarity while dickey
**>fuller ( DF ) and phillips perron ( PP ) ) assume that the
**>null is a unit root. therefore, you should check whetheer
**>the conclusions you get from kpss are consistent with what you
**>would get from DF or PP. the results often are not consistent.
**>
**>also, DF depends on what terms ( trend, constant )
**>you used in your estimation of the model. i'm not sure if kpss
**>does also. people generally report Dickey fuller results but they
**>are a little biased towards acepting unit root ( lower
**>power ) so maybe that's why
**>you are using KPSS ? Eric Zivot has a nice explanation
**>of a lot of the of the stationarity tests in his S+Finmetrics
**>book.
**>
**>testing for cyclical variation is pretty complex because
**>that's basically the same as testing for seasonality.
**>check ord's or ender's book for relatively simple ways of doing that.
**>
**>
**>
**>
**>
**>
**>
**>
**>
**>
**>
**>
**>>
**>>>From: Sachin J
**>>>Date: Thu Jul 06 14:17:25 CDT 2006
**>>>To: R-help@stat.math.ethz.ch
**>>>Subject: [R] KPSS test
**>>
**>>>Hi,
**>>>
**>>> Am I interpreting the results properly? Are my conclusions correct?
**>>>
**>>> > KPSS.test(df)
**>>> ---- ----
**>>> KPSS test
**>>> ---- ----
**>>> Null hypotheses: Level stationarity and stationarity around
**>a linear trend.
**>>> Alternative hypothesis: Unit root.
**>>>----
**>>> Statistic for the null hypothesis of
**>>> level stationarity: 1.089
**>>> Critical values:
**>>> 0.10 0.05 0.025 0.01
**>>> 0.347 0.463 0.574 0.739
**>>>----
**>>> Statistic for the null hypothesis of
**>>> trend stationarity: 0.13
**>>> Critical values:
**>>> 0.10 0.05 0.025 0.01
**>>> 0.119 0.146 0.176 0.216
**>>>----
**>>> Lag truncation parameter: 1
**>>>
**>>>CONCLUSION: Reject Ho at 0.05 sig level - Level Stationary
**>>> Fail to reject Ho at 0.05 sig level - Trend Stationary
**>>>
**>>>> kpss.test(df,null = c("Trend"))
**>>> KPSS Test for Trend Stationarity
**>>> data: tsdata[, 6]
**>>>KPSS Trend = 0.1298, Truncation lag parameter = 1, p-value = 0.07999
**>>>
**>>> CONCLUSION: Fail to reject Ho - Trend Stationary as p-value
**>< sig. level (0.05)
**>>>
**>>>> kpss.test(df,null = c("Level"))
**>>> KPSS Test for Level Stationarity
**>>> data: tsdata[, 6]
**>>>KPSS Level = 1.0891, Truncation lag parameter = 1, p-value = 0.01
**>>> Warning message:
**>>>p-value smaller than printed p-value in: kpss.test(tsdata[,
**>6], null = c("Level"))
**>>>
**>>> CONCLUSION: Reject Ho - Level Stationary as p-value > sig.
**>level (0.05)
**>>>
**>>> Following is my data set
**>>>
**>>> structure(c(11.08, 7.08, 7.08, 6.08, 6.08, 6.08, 23.08, 32.08,
**>>>8.08, 11.08, 6.08, 13.08, 13.83, 16.83, 19.83, 8.83, 20.83, 17.83,
**>>>9.83, 20.83, 10.83, 12.83, 15.83, 11.83), .Tsp = c(2004,
**>2005.91666666667,
**>>>12), class = "ts")
**>>>
**>>> Also how do I test this time series for cyclical varitions?
**>>>
**>>> Thanks in advance.
**>>>
**>>> Sachin
**>>>
**>>>
**>>>---------------------------------
**>>>
**>>> [[alternative HTML version deleted]]
**>>>
**>>>______________________________________________
**>>>R-help@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
**>
**>
**>
**>
**>---------------------------------
**>
**>
**> [[alternative HTML version deleted]]
**>
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