A Sensitivity of the White–Noise Distribution on the Performance of the Bootstrap Method in Uncovering the True Sampling Distribution of Autoregressive Processes

Abstract

Bootstrap is a resampling method of estimating parameters or sampling distributions based on observed data. In order to apply the bootstrap approach when evaluating the parameters of time-series models, we need to consider the lack of independence between the observations. This study addresses the sensitivity of the white-noise distribution to the performance of the bootstrap method in uncovering the true sampling distribution of parameter estimates of autoregressive models. In order to study the performance, we use three white–noise (normal, exponential, and uniform) distributions for three (first and higher-order) models.