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

The paper develops the bootstrap theory and extends the asymptotic theory of rank estimators, such as the Maximum Rank … asymptotic distributions can be consistently estimated by the nonparametric bootstrap. We investigate the accuracy of inference … based on the asymptotic approximation and the bootstrap, and provide bounds on the associated error. In the case of MRC and …

In this paper we analyse bootstrap procedures for systems cointegration tests with a prior adjustment for deterministic … the bootstrap test procedures are derived and their small sample properties are studied. The simulation study also …

describe asymptotically valid nonparametric confidence intervals, using a bootstrap technique. Finally, the proposed measures …

Fluctuation Analysis). Some of these tests exhibit size distortions in small-samples. It is well known that the bootstrap … long memory such as ARFIMA, p-values are calculated using the post-blackening, moving block bootstrap. The Monte Carlo … studies suggest that the bootstrap critical values perform better. The results are applied to financial return time series. …

bootstrap to achieve valid inference in a time series setting. The test statistics and the estimators are computed using linear …

This paper proposes a bootstrap artificial neural network based panel unit root test in a dynamic heterogeneous panel …

their quantile breakdown point. For the block bootstrap and the sub- sampling, we find a very low quantile breakdown point …

bootstrap technique that we use in finite sample settings. A simulation study illustrates the good size and power properties of …