test is a consistent test. We also establish the asymptotic validity of a bootstrap procedure which is used to better …

restrictions on parameters as well as a bootstrap technique to account for the bandwidth’s influence. Our new methods are simple to …

literature for regression models subject to error heteroskedasticity, the Edgeworth expansion for the pairs bootstrap that we …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 …

Since the advent of heteroskedasticity-robust standard errors, several papers have proposed adjustments to the original … samples. We propose a class of alternative heteroskedasticity-robust tests of linear hypotheses based on an Edgeworth …, moderate, and severe levels of heteroskedasticity. …

Several tests for heteroskedasticity in linear regression models are examined. Asymptoticrobustness to heterokurticity … between nominal and actual finite sample significance levels. The use of the bootstrap overcomes this problem for general … approaches that lead to asymptotically pivotal test statistics. Power comparisons are made for bootstrap tests and modified …

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

, 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 …