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]]
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]]
>From: <markleeds at verizon.net> >Date: Thu Jul 06 14:17:25 CDT 2006 >To: Sachin J <sachinj.2006 at yahoo.com> >Subject: Re: [R] KPSS testsachin : 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 <sachinj.2006 at yahoo.com> >>Date: Thu Jul 06 14:17:25 CDT 2006 >>To: R-help at 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 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