Asymptotic properties of sieve bootstrap prediction intervals for processes

2012 ◽  
Vol 82 (12) ◽  
pp. 2108-2114 ◽  
Author(s):  
Maduka Rupasinghe ◽  
V.A. Samaranayake
Keyword(s):  
Asymptotic Properties ◽  
Prediction Intervals ◽  
Sieve Bootstrap

scholarly journals Prediction intervals and regions for multivariate time series models with sieve bootstrap

2018 ◽  
Vol 38 (2) ◽  
pp. 317-357
Author(s):  
Maciej Kawecki ◽  
Roman Różański ◽  
Grzegorz Chłapiński ◽  
Marcin Hławka ◽  
Krzysztof Jamróz ◽  
...  
Keyword(s):  
Time Series ◽  
Simulation Study ◽  
Linear Time ◽  
Multivariate Time Series ◽  
Prediction Intervals ◽  
Time Series Models ◽  
Bootstrap Procedure ◽  
Sieve Bootstrap ◽  
Prediction Regions ◽  
Theoretical Results

In the paper, the construction of unconditional bootstrap prediction intervals and regions for some class of second order stationary multivariate linear time series models is considered. Our approach uses the sieve bootstrap procedure introduced by Kreiss 1992 and Bühlmann 1997. Basic theoretical results concerning consistency of the bootstrap replications and the bootstrap prediction regions are proved. We present a simulation study comparing the proposed bootstrap methods with the Box–Jenkins approach.

scholarly journals Sieve bootstrap prediction intervals

COMPSTAT ◽  
2000 ◽  
pp. 181-186
Author(s):  
Andrés M. Alonso ◽  
Daniel Peña ◽  
Juan Romo
Keyword(s):  
Prediction Intervals ◽  
Sieve Bootstrap

Obtaining prediction intervals for FARIMA processes using the sieve bootstrap

2013 ◽  
Vol 84 (9) ◽  
pp. 2044-2058 ◽  
Author(s):  
Maduka Rupasinghe ◽  
Purna Mukhopadhyay ◽  
V.A. Samaranayake
Keyword(s):  
Prediction Intervals ◽  
Sieve Bootstrap ◽  
Farima Processes

ROBUST SIEVE BOOTSTRAP PREDICTION INTERVALS FOR CONTAMINATED TIME SERIES

2014 ◽  
Vol 28 (07) ◽  
pp. 1460012 ◽  
Author(s):  
GUSTAVO ULLOA ◽  
HÉCTOR ALLENDE-CID ◽  
HÉCTOR ALLENDE
Keyword(s):  
Time Series ◽  
Bootstrap Method ◽  
Time Series Prediction ◽  
Interval Estimation ◽  
Point Estimation ◽  
Point Of View ◽  
Prediction Intervals ◽  
Sieve Bootstrap ◽  
Different Types ◽  
Robust Prediction

Time series prediction is of primary importance in a variety of applications from several science fields, like engineering, finance, earth sciences, etc. Time series prediction can be divided in to two main tasks, point and interval estimation. Estimating prediction intervals, is in some cases more important than point estimation mainly because it indicates the likely uncertainty in the prediction process. Recently, the sieve bootstrap method has been successfully used in prediction of nonlinear time series. In this work, we study the performance of the prediction intervals based on the sieve bootstrap technique, which does not require the distributional assumption of normality as most techniques that are found in the literature. The construction of prediction intervals in the presence of different types of outliers is not robust from a distributional point of view, leading to an undesirable increase in the length of the prediction intervals. In the analysis of time series, it is common to have irregular observations that have different types of outliers. For this reason, we propose the construction of prediction intervals for returns based on the winsorized residual and bootstrap techniques for time series prediction. We propose a novel, simple and distribution free resampling technique for developing robust prediction intervals for returns and volatilities for GARCH models. The proposed procedure is illustrated by an application to known synthetic and real-time series.

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