https://www.machinelearningplus.com/time-series/augmented-dickey-fuller-test/
In 2), why do we only care about $\alpha=1$ to get a unit root. I guess if $\alpha<1$ we have some mean reversion, but why dont we care about $\alpha>1$?
https://www.machinelearningplus.com/time-series/augmented-dickey-fuller-test/
In 2), why do we only care about $\alpha=1$ to get a unit root. I guess if $\alpha<1$ we have some mean reversion, but why dont we care about $\alpha>1$?
It's not that "we don't care". Mathematically, it is not possible to test the null hypothesis $\alpha > 1$, i.e. the model is explosive, against the alternative, say, $\alpha \leq 1$.
One can characterize the distribution of, say, the Dickey-Fuller-type $t$-statistic for each $\alpha > 1$. The limit distribution is a Cauchy distribution. However, the normalizing factor of the statistic depends on $\alpha$, in contrast to the stationary ($|\alpha| < 1$) case.