Unit-root autoregressive process

An autoregressive polynomial with a root at one gives a unit-root model. The basic example $X_t=X_{t-1}+{\epsilon }_t$ is a random walk, whose variance grows with time when the increments have positive variance. Differencing removes this root and recovers its white noise increments.

The Dickey–Fuller test detects a unit-root autoregressive process against a stationary causal alternative. Regress $\Delta X_t$ on $X_{t-1}$, with deterministic terms appropriate to the model. Under the null, the usual regression statistic has a nonnormal Brownian motion functional limit, so ordinary normal critical values are inappropriate.