We present a generalization of the persistent random-walk model in which the step at time n depends on the state of the step at time $n-T,$ for arbitrary T. This gives rise to arbitrarily long memory effects, yet by an appropriate transformation the model is tractable by essentially the same techniques applicable to the usual persistent random-walk problem. We apply our results to the specific case of delayed “step” persistence, and analyze its asymptotic statistical properties.