Complete Subset Averaging for Quantile Regressions
Ji Hyung Lee and
Youngki Shin ()
Department of Economics Working Papers from McMaster University
We propose a novel conditional quantile prediction method based on the complete subset averaging (CSA) for quantile regressions. All models under consideration are potentially misspecified and the dimension of regressors goes to infinity as the sample size increases. Since we average over the complete subsets, the number of models is much larger than the usual model averaging method which adopts sophisticated weighting schemes. We propose to use an equal weight but select the proper size of the complete subset based on the leave-one-out cross-validation method. Building upon the theory of Lu and Su (2015), we investigate the large sample properties of CSA and show the asymptotic optimality in the sense of Li (1987). We check the finite sample performance via Monte Carlo simulations and empirical applications.
Keywords: complete subset averaging; quantile regression; prediction; equal-weight; model averaging (search for similar items in EconPapers)
JEL-codes: C21 C52 C53 (search for similar items in EconPapers)
Pages: 33 pages
New Economics Papers: this item is included in nep-ecm and nep-ore
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Working Paper: Complete Subset Averaging for Quantile Regressions (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:mcm:deptwp:2020-03
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