adf test result clearly wrong and contrast with kpss test. x
I am checking stationarity or non-stationarity of a time series with R and I am using adf.test and kpss.test in tseries package. ADF is a parametric test and KPSS is a non-parametric test of unit root. That being said, the chosen lag order in the ADF should be such that residuals are white noise. Share. ADF and MacKinnon Test, and (iv) an 'urca' Unit Root Test Interface for Pfaff's unit root test suite. 2 Dickey-Fuller p Values The section provides functions to compute the distribution and quantile functions for the ADF unit root test statistics. padf returns the cumulative probability for the ADF test qadf returns the quantiles for theKPSS: The timeseries fails to reject the null hypothesis of stationarity (wtf? how can this timeseries be stationary if it clearly has a tendency upwards?) ADF: Can't reject tau (at 1%) and therefore there is a unit root, reject phi2 and therefore there must be drift, trend or both.
Usually I start with thinking about/reading about/researching the nature of my variables very carefully. If I felt I had to test for some reason, I'd try to avoid testing the specific data I needed a model for, but other, closely related data (e.g. same variable in a different time span, similar/closely related variables etc) Dickey-Fuller Test/Augmented Dickey-Fuller (ADF) Test: This is a statistical test that checks for the presence of a unit root in the time series. If the test indicates that there is no unit root 45uS.