characterize their quantile breakdown point. For the moving block bootstrap and the subsampling, we find a very low quantile …We study the robustness of block resampling procedures for time series. We first derive a set of formulas to … breakdown point. A similar robustness problem arises in relation to data-driven methods for selecting the block size in …

This paper proposes a generalized repeat sales regression (GRSR) that uses repeat sales from the entire market, in …

regression models with Gaussian limit distribution that is instead insensitive to the degree of persistence of the predictors …

We study the problem of estimating the parameters of a linear median regression without any assumption on the shape of … maximizing test p-values and inherit robustness properties from the generating distribution-free tests. Both finite-sample and …

to the assumed logistic regression model, after the set of all true confounders are included. This is done via an … logistic regression. Our results show that in general, adding non-confounders to the regression model decreases the mean …

This paper considers an empirical likelihood method to estimate the parameters of the quantile regression (QR) models … regression models. Our results are extensions of the previous results of Chen and Hall (1993) to the regression contexts. Monte … than the confidence regions that can be constructed from the smoothed bootstrap method recently suggested by Horowitz (1998) …

different bootstrap tests. In the context of static linear regression modelstwo of these are shown to have serious size and … implementations is shown tobreak down in dynamic regression models. Then the procedure based on the test statistic approachperforms …

-range dependencies are present only in the intraday volatility but not in the intraday returns. Finally, the robustness of these findings …