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I have a time series data for 18 months. To check for stationary I conducted adf test, to which my p value is 0.8. And kpss test has a p value of 0.1 , so at 95% confidence level I fail to reject null in both cases. That means my adf test confirm that data is non stationary whereas kpss test confirms my data as stationary. Why is that happening?

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For the data you have, neither test is powerful enough to reject the null hypothesis. But it looks like KPSS test is not too far from rejecting it; a little more power (a little more data), and that might have happened. If so, the results of the ADF and KPSS tests would not be contradictory anymore. So I do not see a big disagreement in the test results in your application.

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  • $\begingroup$ Thanks Richard. $\endgroup$
    – Manisha
    Dec 4, 2019 at 14:27
  • $\begingroup$ Do we have any other powerful test to have a confirmation? $\endgroup$
    – Manisha
    Dec 4, 2019 at 14:28
  • $\begingroup$ There are some other tests like the ADF-GLS test which is likely more powerful than the vanilla ADF test, but sometimes the sample is simply too small to allow for inference at high levels of confidence. $\endgroup$ Dec 4, 2019 at 16:56
  • $\begingroup$ Okay,thanks so much Richard for your time $\endgroup$
    – Manisha
    Dec 4, 2019 at 18:47
  • $\begingroup$ @Manisha, thank you, I am glad to have helped. $\endgroup$ Dec 4, 2019 at 21:16

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