compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models …

rejection regions can be calculated by a simple parametric bootstrap procedure, when the sample size is small. The size and …

-defined. Several bootstrap procedures are proposed. They alleviate the problem and allow reliable inference when the instruments are …

We examine the performance of asymptotic inference as well as bootstrap tests for the Alphabeta and Kobus-Miłoś family … statistical power of asymptotic inference and the Studentized bootstrap test. In a broad variety of settings, both tests are found … inequality, whereas the bootstrap test becomes somewhat oversized in these extreme settings. …

bootstrap, in the case of inequality indices. To estimate the parameters of the assumed parametric data generating distribution … inequality index. Its primary advantage is that the scale parameter does not need to be estimated to perform parametric bootstrap … suggest that this feature provides an advantage over the parametric bootstrap using the maximum likelihood estimator. We also …

This paper evaluates bootstrap inference methods for quantile regression panel data models. We propose to construct … confidence intervals for the parameters of interest using percentile bootstrap with pairwise resampling. We study three different … bootstrapping procedures. First, the bootstrap samples are constructed by resampling only from cross-sectional units with …

-run asymmetry are generally oversized if the threshold parameter is estimated by conditional least squares and show that bootstrap …

normality. Bootstrap inference can be expected to be more reliable, and appropriate bootstrap procedures are proposed. As an … enough for asymptotic and bootstrap inference to be almost identical, but that, in the twenty-first century, the bootstrap …