Bootstrap predictive inference for ARIMA processes
提出一种新的Bootstrap策略来获取自回归积分滑动平均过程的预测区间，该方法能纳入参数估计的变异性，且实现简单，在多数情况下表现优于或等于现有方法。
Abstract. In this study, we propose a new bootstrap strategy to obtain prediction intervals for autoregressive integrated moving‐average processes. Its main advantage over other bootstrap methods previously proposed for autoregressive integrated processes is that variability due to parameter estimation can be incorporated into prediction intervals without requiring the backward representation of the process. Consequently, the procedure is very flexible and can be extended to processes even if their backward representation is not available. Furthermore, its implementation is very simple. The asymptotic properties of the bootstrap prediction densities are obtained. Extensive finite‐sample Monte Carlo experiments are carried out to compare the performance of the proposed strategy vs. alternative procedures. The behaviour of our proposal equals or outperforms the alternatives in most of the cases. Furthermore, our bootstrap strategy is also applied for the first time to obtain the prediction density of processes with moving‐average components.