In this paper we are interested in inference based on heteroskedasticity consistent covariance matrix estimators, for which the appropriate bootstrap is a version of the wild bootstrap. Simulation results, obtained by a new very efficient methos, show that all wild bootstraps tests exhibit...

Recent results of Cribari-Neto and Zarkos show that bootstrap methods can be successfully used to estimate a heteroskedasticity robust covariance matrix estimator. We show that their bootstrap estimator can be calculated directly, without bootstrapping, and that inference based on it may not be...

The paper is concerned with the estimation of the long memory parameter in a conditionally heteroskedastic model proposed by Giraitis, Robinson and Surgailis (1999). We consider methods based on the partial sums of the squared observations which are similar in spirit to the classicla R/S...

This paper compares the power in small samples of different tests for conditional heteroscedasticity. Two new tests, based on neural networks, are proposed: the main interest in them arises from the fact that they do not require the exact specification of the conditional variance under the...

In this paper we are interested in heteroskedastic regression model s, for which an appropriate bootstrap method is bootstrapping pairs, proposed by Freedman (1981). We propose an ameliorate version of it, with better numerical performance.

In this paper, we analyse the behaviour of regression-based tests for seasonal unit roots when the error is periodically heteroscedastic. We show, using the case of quaterly data to illustrate, that the limiting null distribution of tests for unit roots at the zero and Nyquist frequencies are...