  This paper proposes a new bootstrap method to compute predictive intervals
for nonlinear autoregressive time series model forecast. This method we call
the splice boobstrap as it involves splicing the last p values of a given
series to a suitably simulated series. This ensures that each simulated series
will have the same set of p time series values in common, a necessary
requirement for computing conditional predictive intervals. Using simulation
studies we show the methods gives 90% intervals intervals that are similar to
those expected from theory for simple linear and SETAR model driven by normal
and non-normal noise. Furthermore, we apply the method to some economic data
and demonstrate the intervals compare favourably with cross-validation based
intervals.
