2013/11/22 by Gerard Keogh, Keogh, Gerard
Mathematics · #FOS: Computer and information sciences #Methodology (stat.ME) #stat.ME
paper · pdf · doi:10.48550/arxiv.1311.5828
16 pages and 2 large appendix tables with simulation results
arxiv created 2013/11/22 · arxiv updated 2013/11/25
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.