Mixing Property and Functional Central Limit Theorems for a Sieve Bootstrap in Time Series

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...

Sieve bootstrap with variable length Markov chains for stationary categorical time series

Sieve Bootstrap for Time Series Sieve Bootstrap for Time Series

We study a bootstrap method which is based on the method of sieves. A linear process is approximated by a sequence of autoregressive processes of order p = pn, where pn ! 1 ; p n = on as the sample size n ! 1. F or given data, we t h e n estimate such a n A R pn model and generate a bootstrap sample by resampling from the residuals. This sieve bootstrap enjoys a nice nonparametric property. We ...

Functional-Coefficient Autoregressive Model and its Application for Prediction of the Iranian Heavy Crude Oil Price

Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...

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...