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 independent and identically distributed innovations
$\xi _n$ take only two values
$\pm 1$ and
$a \le \tfrac 23$. This limiting distribution is quasi-stationary. It has no atoms and is singular with respect to the Lebesgue measure when
$\tfrac 12< a \le \tfrac 23$, except for the case when
$a=\tfrac 23$ and
$\mathbb P(\xi _n=1)=\tfrac 12$, where this distribution is uniform on the interval
$[0,3]$. This is similar to the properties of Bernoulli convolutions. For
$0 < a \le \tfrac 12$, the situation is much simpler and the limiting distribution is a
$\delta $-measure. To prove these results, we uncover a close connection between
$X_n$ killed at exiting
$[0, \infty )$ and the classical dynamical system defined by the piecewise linear mapping
$x \mapsto x/a + 1/2\ \pmod 1$. Namely, the trajectory of this system started at
$X_n$ deterministically recovers the values of the killed chain in reversed time. We use this fact to construct a suitable Banach space, where the transition operator of the killed chain has the compactness properties that allow us to apply a conventional argument of the Perron–Frobenius type.