, 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 …

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 …

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. …

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 …

validity of a local smoothed bootstrap that we use in finite sample settings to compute a bootstrap bias-corrected estimator … and to perform statistical tests. A Monte Carlo simulation study reveals that the bootstrap bias-corrected estimator …