Bootstrap prediction intervals for Markov processes

Given time series data X1,…,Xn, the problem of optimal prediction of Xn+1 has been well-studied. The same is not true, however, as regards the problem of constructing a prediction interval with prespecified coverage probability for Xn+1, i.e., turning the point predictor into an interval predictor. In the past, prediction intervals have mainly been constructed for time series that obey an autoregressive model that is linear, nonlinear or nonparametric. In the paper at hand, the scope is expanded by assuming only that {Xt} is a Markov process of order p≥1 without insisting that any specific autoregressive equation is satisfied. Several different approaches and methods are considered, namely both Forward and Backward approaches to prediction intervals as combined with three resampling methods: the bootstrap based on estimated transition densities, the Local Bootstrap for Markov processes, and the novel Model-Free bootstrap. In simulations, prediction intervals obtained from different methods are compared in terms of their coverage level and length of interval.