Among these, our focus was on the HEGY tests, which is effectively an extension of the ADF test in the direction of non-zero seasonal frequencies, and the CH test, which is the analogue of the KPSS test in the direction of non-zero seasonal frequencies. We also looked at some of the mathematical details which underly these methods. 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) 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. So, this indicates that the time series is stationary. But after doing adf.test and Kpss test, the result tells difference story. Augmented Dickey-Fuller Test data: ltc.ts Dickey-Fuller = 1.7982, Lag order = 3, p-value = 0.99 alternative hypothesis: stationary P-value of adf.test is 0.99 and I can not reject the null hypothesis : non-stationary Your job is to copy the R code above and paste in the R console. This will create a R function called "adf", which runs the unit root test for each case. You should use the ADF test for each individual series (chickens and eggs), controlling for the number of lags, and the inclusion of constants and trends. The thesis focusses on the KPSS test and the ADF test and both review cases with and without a trend. The goal is to bring additional knowledge of whether one of the tests are more reliable in terms of size and power and when contradictory results occur. The result shows that both KPSS and ADF suffer from low power and size distortion l4za.

kpss test vs adf test