Bootstrap confidence intervals for impulse responses computed from autoregressive processes are considered. A detailed analysis of the methods in current use shows that they are not very reliable in some cases. In particular, there are theoretical reasons for them to have actual coverage probabilities which deviate considerably from the nominal level in some situations of practical importance. For a simple case alternative bootstrap methods are proposed which provide correct results asymptotically
It is common in parametric bootstrap to select the model from the data, and then treat it as it were...
Theory in time series analysis is often developed in the context of finite-dimensional models for th...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...
Bootstrap confidence intervals for impulse responses computed from autoregressive processes are cons...
Bootstrap condence intervals for impulse responses computed from autoregressive processes are consid...
It is argued that standard impulse response analysis based on vector autoregressive models has a num...
We prove geometric ergodicity and absolute regularity of the nonparametric autoregressive bootstrap ...
This paper contributes to a growing literature on confidence interval construction for impulse respo...
We propose a new bootstrap algorithm for inference for impulse responses in structural vector autore...
We examine the theory and behavior in practice of Bayesian and bootstrap methods for generating erro...
This Article Investigates The Construction Of Skewness-Adjusted Confidence Intervals And Joint Conf...
This paper examines the problem of testing and confidence set construction for one-dimensional funct...
Constructing bootstrap confidence intervals for impulse response functions (IRFs) from structural ve...
In this paper, we consider residual-based bootstrap methods à la GonÇalves and Perron (2014) to cons...
This paper examines the performance of prediction intervals based on bootstrap for threshold autoreg...
It is common in parametric bootstrap to select the model from the data, and then treat it as it were...
Theory in time series analysis is often developed in the context of finite-dimensional models for th...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...
Bootstrap confidence intervals for impulse responses computed from autoregressive processes are cons...
Bootstrap condence intervals for impulse responses computed from autoregressive processes are consid...
It is argued that standard impulse response analysis based on vector autoregressive models has a num...
We prove geometric ergodicity and absolute regularity of the nonparametric autoregressive bootstrap ...
This paper contributes to a growing literature on confidence interval construction for impulse respo...
We propose a new bootstrap algorithm for inference for impulse responses in structural vector autore...
We examine the theory and behavior in practice of Bayesian and bootstrap methods for generating erro...
This Article Investigates The Construction Of Skewness-Adjusted Confidence Intervals And Joint Conf...
This paper examines the problem of testing and confidence set construction for one-dimensional funct...
Constructing bootstrap confidence intervals for impulse response functions (IRFs) from structural ve...
In this paper, we consider residual-based bootstrap methods à la GonÇalves and Perron (2014) to cons...
This paper examines the performance of prediction intervals based on bootstrap for threshold autoreg...
It is common in parametric bootstrap to select the model from the data, and then treat it as it were...
Theory in time series analysis is often developed in the context of finite-dimensional models for th...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...