Percentile and Percentile-t Bootstrap Confidence Intervals: A Practical Comparison

Abstract

Abstract This paper employs a Monte Carlo study to compare the performance of equal-tailed bootstrap percentile-t, symmetric bootstrap percentile-t, bootstrap percentile, and standard asymptotic confidence intervals in two distinct heteroscedastic regression models. Bootstrap confidence intervals are constructed with both the XY and wild bootstrap algorithm. Theory implies that the percentile-t methods will outperform the other methods, where performance is based on the convergence rate of empirical coverage to the nominal level. Results are consistent across models, in that in the case of the XY bootstrap algorithm the symmetric percentile-t method outperforms the other methods, but in the case of the wild bootstrap algorithm the two percentile-t methods perform similarly and outperform the other methods. The implication is that practitioners that employ the XY algorithm should utilize the symmetric percentile-t interval, while those who opt for the wild algorithm should use either of the percentile-t methods.