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
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...
We introduce a new bootstrap strategy to obtain prediction intervals inARIMA (P,d,l) processes. Its ...
In this paper we propose a bootstrap resampling scheme to construct prediction intervals for future ...
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 specification of multivariate prediction regions, having coverage probability closed to the targ...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
The theory and methodology of obtaining bootstrap prediction intervals for univariate time series us...
I propose principles and methods for the construction of a time-simultaneous prediction band for a u...
This paper concerns the specification of multivariate prediction regions which may be useful in time...
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
Many statistical applications require the forecast of a random variable of interest over several per...
The traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres ass...
<p>Making predictions of future realized values of random variables based on currently available dat...
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...
We introduce a new bootstrap strategy to obtain prediction intervals inARIMA (P,d,l) processes. Its ...
In this paper we propose a bootstrap resampling scheme to construct prediction intervals for future ...
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 specification of multivariate prediction regions, having coverage probability closed to the targ...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
The theory and methodology of obtaining bootstrap prediction intervals for univariate time series us...
I propose principles and methods for the construction of a time-simultaneous prediction band for a u...
This paper concerns the specification of multivariate prediction regions which may be useful in time...
Prediction intervals in state space models can be obtained by assuming Gaussian innovations and usin...
Many statistical applications require the forecast of a random variable of interest over several per...
The traditional Box-Jenkins approach to obtaining prediction intervals for stationary time seres ass...
<p>Making predictions of future realized values of random variables based on currently available dat...
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...
We introduce a new bootstrap strategy to obtain prediction intervals inARIMA (P,d,l) processes. Its ...
In this paper we propose a bootstrap resampling scheme to construct prediction intervals for future ...