I am rather confused about the alternative hypothesis of the Dickey-Fuller test (at least from a practical perspective).
I understand the mathematical details behind the DF test, but I am struggling to understand the consequences of rejecting the null hypothesis.
Say that we have a time series with an upward facing (linear) trend. We don't know a priori if this is a stationary process around a time trend or a random walk with a drift.
If we use the following specification of the DF test:
$$\Delta Y(t)=\beta_1+\gamma\cdot Y(t-1)+e(t)$$
and the null hypothesis is rejected then the alternative is that the process $Y(t)$ is stationary with non zero mean $\frac{\beta_1}{1+\gamma}$.
The problem is that this test is not comparing "deterministic trend" versus "stochastic trend". Like, if you reject the null hypothesis I mentioned above, the alternative is not "suitable" since we know that the series has an upward facing trend.
Am I missing something?