This paper presents a technique to estimate the average time separation of events (TSE) in stochastic timed Petri nets that can model time-independent choice and have arbitrary delay distributions associated with places. The approach analyzes finite net unfoldings to derive closed-form expressions for lower and upper bounds on the average TSE, which can be efficiently evaluated using standard statistical methods. The mean of the derived upper and lower bounds thus provides an estimate of the average TSE which has a well-defined error bound. Moreover, we can often make the error arbitrarily small by analyzing larger net unfoldings at the cost of additional run-time. Experiments on several asynchronous systems demonstrate the quality of our estimate and the efficiency of the technique. The experiments include the performance analysis of a full-scale Petri net model of Intel's asynchronous instruction length decoding and steering unit RAPPID containing over 900 transitions and 500 places.