This is a question about statistics theory, not about R, so it is off topic
here... you might try discussing this at the CrossValidated forum. In general,
the amount of lag that is relevant depends on how you intend to use the data.
This is not unlike the application of linear models to nonlinear processes when
only a very limited domain is of interest.
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Sent from my phone. Please excuse my brevity.
On March 28, 2014 6:31:23 PM PDT, ritesh_061176 <ritesh.ku...@mail.mcgill.ca>
wrote:
>I have a time series with 1100 rows of data and am getting
>contradicting
>results from these tests:1) My ACF and PACF plots have lot of lags and
>upon
>looking visually, it looks as if the series in not-stationary.2) But
>when I
>do the ADF test with with lag=30, I reject the null and conclude that
>series
>is stationary.3) When I do the ADF with 100 lags, I end up accepting
>the
>null and have to interpret that series is non stationary.Which one
>shall I
>choose here ? What is the correct way to go about in these situations.
>
>
>
>
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