Persistence of AR($1$) sequences with Rademacher innovations and linear mod $1$ transforms

Abstract: We study the probability that an AR(1) Markov chain $X_{n+1}=aX_n+\xi_{n+1}$, where $a\in(0,1)$ is a constant, stays non-negative for a long time. We find the exact asymptotics of this probability and the weak limit of $X_n$ conditioned to stay non-negative, assuming that the i.i.d.\ innovations $\xi_n$ take only two values $\pm1$ and $a \le \frac23$. This limiting distribution is quasi-stationary. It has no atoms and is singular with respect to the Lebesgue measure when $\frac12< a \le \frac23$, except for the case $a=\frac23$ and $\mathbf{P}(\xi_n=1)=\frac12$, where this distribution is uniform on the interval $[0,3]$. This is similar to the properties of Bernoulli convolutions. It turns out that for the $\pm1 $ innovations there is a close connection between $X_n$ killed at exiting $[0, \infty)$ and the classical dynamical system defined by the piecewise linear mapping $x \mapsto \frac1a x + \frac12 \pmod 1$. Namely, the trajectory of this system started at $X_n$ deterministically recovers the values of the killed chain in reversed time! We use this property to construct a suitable Banach space, where the transition operator of the killed chain has the compactness properties that let us apply a conventional argument of Perron--Frobenius type. The difficulty in finding such space stems from discreteness of the innovations.