On sieve bootstrap prediction intervals

Neural network sieve bootstrap prediction intervals for hydrological time series

When analyzing time series data, the estimation of forecast intervals, based on an observed sample path of the process, is a key issue. If the process is linear and the distribution of the error process is known, the methodology is well developed but, for departures from the true underlying distribution, the prediction intervals perform poorly. In this latter case several distribution free alte...

JMASM10: A Fortran Routine For Sieve Bootstrap Prediction Intervals

Semiparametric Bootstrap Prediction Intervals in time Series

One of the main goals of studying the time series is estimation of prediction interval based on an observed sample path of the process. In recent years, different semiparametric bootstrap methods have been proposed to find the prediction intervals without any assumption of error distribution. In semiparametric bootstrap methods, a linear process is approximated by an autoregressive process. The...

On the Consistency of Sieve Bootstrap Prediction Intervals for Stationary Time Series

In the article, we consider construction of prediction intervals for stationary time series using Bühlmann’s [8], [9] sieve bootstrap approach. Basic theoretical properties concerning consistency are proved. We extend the results obtained earlier by Stine [21], Masarotto and Grigoletto [13] for an autoregressive time series of finite order to the rich class of linear and invertible stationary m...

A Sieve Bootstrap approach to constructing Prediction Intervals for Long Memory Time series

This paper is concerned with the construction of bootstrap prediction intervals for autoregressive fractionally integrated movingaverage processes which is a special class of long memory time series. For linear short-range dependent time series, the bootstrap based prediction interval is a good nonparametric alternative to those constructed under parameter assumptions. In the long memory case, ...