This Article Investigates The Construction Of Skewness-Adjusted Confidence Intervals And Joint Confidence Bands For Impulse Response Functions From Vector Autoregressive Models. Three Different Implementations Of The Skewness Adjustment Are Investigated. The Methods Are Based On A Bootstrap Algorithm That Adjusts Mean And Skewness Of The Bootstrap Distribution Of The Autoregressive Coefficients Before The Impulse Response Functions Are Computed. Using Extensive Monte Carlo Simulations, The Methods Are Shown To Improve The Coverage Accuracy In Small And Medium Sized Samples And For Unit Root Processes For Both Known And Unknown Lag Orders
It is argued that standard impulse response analysis based on vector autoregressive models has a num...
It is common in parametric bootstrap to select the model from the data, and then treat it as it were...
The problem of forecasting from vector autoregressive models has attracted considerable attention in...
Constructing joint confidence bands for structural impulse response functions based on a VAR model i...
The statistical reliability of estimated VAR impulse responses is an important concern in applied wo...
In vector autoregressive analysis confidence intervals for individual impulse responses are typicall...
Constructing bootstrap confidence intervals for impulse response functions (IRFs) from structural ve...
Methods for constructing joint confidence bands for impulse response functions which are commonly u...
We examine the theory and behavior in practice of Bayesian and bootstrap methods for generating erro...
This paper investigates the finite sample properties of confidence intervals for structural vector e...
This paper proposes a new non-parametric method of constructing joint confidence bands for impulse r...
In impulse response analysis estimation uncertainty is typically displayed by constructing bands aro...
In this paper, we consider residual-based bootstrap methods à la GonÇalves and Perron (2014) to cons...
Bootstrap confidence intervals for impulse responses computed from autoregressive processes are cons...
It is argued that standard impulse response analysis based on vector autoregressive models has a num...
It is common in parametric bootstrap to select the model from the data, and then treat it as it were...
The problem of forecasting from vector autoregressive models has attracted considerable attention in...
Constructing joint confidence bands for structural impulse response functions based on a VAR model i...
The statistical reliability of estimated VAR impulse responses is an important concern in applied wo...
In vector autoregressive analysis confidence intervals for individual impulse responses are typicall...
Constructing bootstrap confidence intervals for impulse response functions (IRFs) from structural ve...
Methods for constructing joint confidence bands for impulse response functions which are commonly u...
We examine the theory and behavior in practice of Bayesian and bootstrap methods for generating erro...
This paper investigates the finite sample properties of confidence intervals for structural vector e...
This paper proposes a new non-parametric method of constructing joint confidence bands for impulse r...
In impulse response analysis estimation uncertainty is typically displayed by constructing bands aro...
In this paper, we consider residual-based bootstrap methods à la GonÇalves and Perron (2014) to cons...
Bootstrap confidence intervals for impulse responses computed from autoregressive processes are cons...
It is argued that standard impulse response analysis based on vector autoregressive models has a num...
It is common in parametric bootstrap to select the model from the data, and then treat it as it were...
The problem of forecasting from vector autoregressive models has attracted considerable attention in...