, residual-based bootstrap methods are introduced for asymptotically refined approximations to the finite sample critical values … conditions are not fully met, bootstrap may lead to unstable critical values that change significantly with the alternative …, whereas when all conditions are met, bootstrap critical values are very stable, approximate much better the finite sample …

function representations computed on each bootstrap sample, thereby reducing computational time considerably. This method is … improvement in the numerical speed of the fast bootstrap method. …

violation of such assumptions can lead to serious modelling flaws. We propose here a bootstrap-corrected conditional moment …

regression model. Monte Carlo simulations in two settings where the bootstrap fails show the accuracy and robustness of the …

-nested alternatives. The test is an extension of the classical J test for non-nested regression models. I also provide a bootstrap version …

We propose a bootstrap method for statistics that are a function of multivariate high frequency returns such as … realized regression, covariance and correlation coefficients. We show that the finite sample performance of the bootstrap is … superior to the existing first-order asymptotic theory. Nevertheless, and contrary to the existing results in the bootstrap …

This paper is concerned with the use of the bootstrap for statistics in spatial econometric models, with a focus on the …, the bootstrap can be studied based on linear–quadratic (LQ) forms of disturbances. By proving the uniform convergence of … the cumulative distribution function for LQ forms to that of a normal distribution, we show that the bootstrap is …

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 …