Multi-Step-Ahead prediction intervals for nonparametric autoregressions via Bootstrap: consistency, debiasing, and pertinence
We consider a forward bootstrap approach with non-parametric model estimator to resolve the difficulty on predicting multi-step ahead time series data. We construct a quantile prediction interval that is asymptotically valid. Moreover, after taking a debiasing technique, we can build pertinent prediction intervals in which the estimation variability is captured. (This is my first paper with my advisor Professor Politis. Thanks for his guidance!)