# The question is related to the regression analysis - stationarity testing

How to interpret different scenarios in ADF test.

Scenarios: ADF Test: Type: None, Drift, Trend

What exactly each of the types specify and when to use which 'type' during performing stationarity and what is the significance of type?

what happens if the test results says that there is a drift or there is a trend and drift in the time series, how do I interpret these results?

For Eg: URCA package of R has following

• Take the underlying regression model: $\Delta y_t=\alpha+\beta \,t+\delta_1 \Delta y_{t-1}+\delta_2 \Delta y_{t-2}+\dots+\delta_{m-1} \Delta y_{t-m+1}+\epsilon_t$, None corresponds to $\alpha=0$ and $\beta=0$,and Drift to $\beta=0$, whilst Trend means both $\alpha$ and $\beta$ are included. – Magic is in the chain Sep 12 '20 at 19:14
• That I understood, i need what is the significance, and why do I need to test all these scenarios while testing for adf. – syed tabrez Sep 13 '20 at 10:10