Add to ORKGIn this paper we propose bootstrap methods for constructing nonparametric prediction intervals for a general class of linear processes. Our approach uses the AR(∞)-sieve bootstrap procedure based on residual resampling from an autoregressive approximation to the given process. We present a Monte Carlo study comparing the 2nite sample properties of the sieve bootstrap with those of alternative methods. Finally, we illustrate the performance of the proposed method wit
In the paper, the construction of unconditional bootstrap prediction intervals and regions for some...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...
In this paper we propose bootstrap methods for constructing nonparametric prediction intervals for a...
In this paper we consider bootstrap methods for constructing nonparametric prediction intervals for ...
In this paper we propose bootstrap methods for constructing nonparametric prediction intervals for a...
In this paper we consider a sieve bootstrap method for constructing nonparametric prediction interva...
The traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres ass...
We study a bootstrap method which is based on the method of sieves. A linear process is approximated...
Traditional Box-Jenkins prediction intervals perform poorly when the innovations are not Gaussian. N...
A Fortran routine for constructing nonparametric prediction intervals for a general class of linear ...
The sieve bootstrap is a resampling technique that uses autoregressive approximations of order p to ...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
The sieve bootstrap (SB) prediction intervals for invertible autoregressive moving average (ARMA) pr...
A new method to construct nonparametric prediction intervals for nonlinear time series data is propo...
In the paper, the construction of unconditional bootstrap prediction intervals and regions for some...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...
In this paper we propose bootstrap methods for constructing nonparametric prediction intervals for a...
In this paper we consider bootstrap methods for constructing nonparametric prediction intervals for ...
In this paper we propose bootstrap methods for constructing nonparametric prediction intervals for a...
In this paper we consider a sieve bootstrap method for constructing nonparametric prediction interva...
The traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres ass...
We study a bootstrap method which is based on the method of sieves. A linear process is approximated...
Traditional Box-Jenkins prediction intervals perform poorly when the innovations are not Gaussian. N...
A Fortran routine for constructing nonparametric prediction intervals for a general class of linear ...
The sieve bootstrap is a resampling technique that uses autoregressive approximations of order p to ...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
The sieve bootstrap (SB) prediction intervals for invertible autoregressive moving average (ARMA) pr...
A new method to construct nonparametric prediction intervals for nonlinear time series data is propo...
In the paper, the construction of unconditional bootstrap prediction intervals and regions for some...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...