Consider a sequence of i.i.d. random Lipschitz functions ${Psi_n}_{n geq 0}$. Using this sequence we can define a Markov chain via the recursive formula $R_{n+1} = Psi_{n+1}(R_n)$. It is a well known fact that under some mild moment assumptions this
Markov chain has a unique stationary distribution. We are interested in the tail behaviour of this distribution in the case when $Psi_0(t) approx A_0t+B_0$. We will show that under subexponential assumptions on the random variable $log^+(A_0vee B_0)$ the tail asymptotic in question can be described using the integrated tail function of $log^+(A_0vee B_0)$. In particular we will obtain new results for the random difference equation $R_{n+1} = A_{n+1}R_n+B_{n+1}$..
