We study the robustness of block resampling procedures for time series. We first derive a set of formulas to quantify … their quantile breakdown point. For the block bootstrap and the sub- sampling, we find a very low quantile breakdown point …. A similar robustness problem arises in relation to data-driven methods for selecting the block size in applications …

This paper uses Monte Carlo simulation analysis to study the finite-sample behavior of bootstrap estimators and tests … tailored to handle heteroskedasticity. Our results show that weighted bootstrap methods can be successfully used to estimate … test and of Bartlett and Edgeworth-corrected tests. The bootstrap test was found to be robust against unfavorable …

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 …

regression models with independent and identically distributed error terms, appropriately chosen bootstrap methods generally work … very well. However, there are many other cases, such as regression models with dependent errors, in which bootstrap methods …There are many bootstrap methods that can be used for econometric analysis. In certain circumstances, such as …

result is then used to prove that two different bootstrap procedures (one based on sub-sampling, the other based on smoothing … one input and one output, and previous bootstrap methods proposed for inference have not been proven consistent, making …) provide consistent inference. The smooth bootstrap requires smoothing the irregularly-bounded density of inputs and outputs as …

under are highly nonstandard due to the inherent irregular natures of the problem, and then construct bootstrap critical …

conditional heteroskedasticity. We show that the wild bootstrap provides convenient critical values for the considered OLS …-based statistics under both homoskedastic and conditionally heteroskedastic model errors. The wild bootstrap is easy to implement and … approximations. We prove further that the wild bootstrap retains its validity for inference within a system of pooled equations …