R help - Apr 2017 - Augmented Dickey Fuller test

Dear all,

I am trying to run an ADF test using the adf.test() function in the tseries
package and the ur.df() function in the urca package. The results I get contrast
sharply. Whilst the adf.test() indicates stationarity which is in line with the
corresponding graph, the ur.df() indicates non-stationarity.



Why does this happen? Could anybody explain the adf.test() function in more
detail? How does adf.test() select the number of lags is it AIC or BIC and how
does it take an intercept and/or a trend into account?



Help is greatly appreciated.



Thanks in advance.

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On Fri, 28 Apr 2017, T.Riedle wrote:
> Dear all,
>
> I am trying to run an ADF test using the adf.test() function in the 
> tseries package and the ur.df() function in the urca package. The 
> results I get contrast sharply. Whilst the adf.test() indicates 
> stationarity which is in line with the corresponding graph, the ur.df() 
> indicates non-stationarity.
>
> Why does this happen?
This is likely due to different setting for the deterministic part of the 
model and/or the number of lags tested. The defaults of ur.df() are often 
not suitable for many practical applications which might to spurious 
significant results.
> Could anybody explain the adf.test() function in more detail? How does 
> adf.test() select the number of lags is it AIC or BIC and how does it 
> take an intercept and/or a trend into account?
There is a deterministic trend and the default number of lags is selected 
by a heuristic.

At

https://stats.stackexchange.com/questions/168332/r-augmented-dickey-fuller-adf-test/168355#168355

I've summarized an overview that I had written for my students. It might 
also be helpful for you.

hth,
Z