This paper deals with simultaneous prediction for time series models. In particular, it presents a simple procedure which gives well-calibrated simultaneous prediction intervals with coverage probability close to the target nominal value. Although the exact computation of the proposed intervals is usually not feasible, an approximation can be easily attained by means of a suitable bootstrap simulation procedure. This new predictive solution is much simpler to compute than those ones already proposed in the literature, based on asymptotic calculations. Applications of the bootstrap calibrated procedure to AR, MA and ARCH models are presented
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
In this paper, we propose a bootstrap procedure to construct prediction intervals for future values ...
A Fortran routine for constructing nonparametric prediction intervals for a general class of linear ...
This paper deals with simultaneous prediction for time series models. In particular, it presents a s...
This paper deals with simultaneous prediction for time series models. In particular, it presents a ...
This article concerns the construction of prediction intervals for time series models. The estimativ...
The theory and methodology of obtaining bootstrap prediction intervals for univariate time series us...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
The traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres ass...
I propose principles and methods for the construction of a time-simultaneous prediction band for a u...
This paper examines the performance of prediction intervals based on bootstrap for threshold autoreg...
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...
The specification of multivariate prediction regions, having coverage probability closed to the targ...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...
We introduce a new bootstrap strategy to obtain prediction intervals inARIMA (P,d,l) processes. Its ...
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
In this paper, we propose a bootstrap procedure to construct prediction intervals for future values ...
A Fortran routine for constructing nonparametric prediction intervals for a general class of linear ...
This paper deals with simultaneous prediction for time series models. In particular, it presents a s...
This paper deals with simultaneous prediction for time series models. In particular, it presents a ...
This article concerns the construction of prediction intervals for time series models. The estimativ...
The theory and methodology of obtaining bootstrap prediction intervals for univariate time series us...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
The traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres ass...
I propose principles and methods for the construction of a time-simultaneous prediction band for a u...
This paper examines the performance of prediction intervals based on bootstrap for threshold autoreg...
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...
The specification of multivariate prediction regions, having coverage probability closed to the targ...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...
We introduce a new bootstrap strategy to obtain prediction intervals inARIMA (P,d,l) processes. Its ...
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
In this paper, we propose a bootstrap procedure to construct prediction intervals for future values ...
A Fortran routine for constructing nonparametric prediction intervals for a general class of linear ...