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: [EMAIL PROTECTED] >-----Ursprüngliche Nachricht----- >Von: [EMAIL PROTECTED] >[mailto:[EMAIL PROTECTED] Im Auftrag von Sachin J >Gesendet: Donnerstag, 6. Juli 2006 21:49 >An: [EMAIL PROTECTED] >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 > > > >[EMAIL PROTECTED] 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]] > >______________________________________________ >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 > ***************************************************************** Confidentiality Note: The information contained in this mess...{{dropped}} ______________________________________________ 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