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]]

