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• W3098817453 abstract "We study the small sample properties of conditional quantile estimators such as classical and IV quantile regression. First, we propose a higher-order analytical framework for comparing competing estimators in small samples and assessing the accuracy of common inference procedures. Our framework is based on a novel approximation of the discontinuous sample moments by a Holder-continuous process with a negligible error. For any consistent estimator, this approximation leads to asymptotic linear expansions with nearly optimal rates. Second, we study the higher-order bias of exact quantile estimators up to $Oleft(frac{1}{n}right)$. Using a novel non-smooth calculus technique, we uncover previously unknown non-negligible bias components that cannot be consistently estimated and depend on the employed estimation algorithm. To circumvent this problem, we propose a symmetric bias correction, which admits a feasible implementation. Our simulations confirm the empirical importance of bias correction." @default.
• W3098817453 created "2020-11-23" @default.
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• W3098817453 date "2020-11-05" @default.
• W3098817453 modified "2023-09-23" @default.
• W3098817453 title "Conditional quantile estimators: A small sample theory" @default.
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• W3098817453 hasPublicationYear "2020" @default.
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