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
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