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Title: Conditional quantile coverage: An application to growth at risk Authors:  Valentina Corradi - University of Surrey (United Kingdom) [presenting]
Abstract: Tests for pairwise and multiple out-of-sample comparisons of parametric conditional quantile models are proposed. The tests rank the distance between actual and nominal conditional coverage w.r.t. the union of information sets across models, for a given loss function. Our approach operates uniformly over a compact set of quantile ranks, thereby assessing models' relative forecast ability across different quantile subsets. We derive the limiting distribution and establish the first-order validity of block bootstrap critical values. An empirical application to Growth-at-Risk (GaR) uncovers situations where a threshold quantile model improves over the standard linear quantile regression approach.