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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, <jats:italic>Journal of Econometrics<\/jats:italic> 188, 40\u201358), we investigate the large sample properties of CSA and show the asymptotic optimality in the sense of Li (1987, <jats:italic>Annals of Statistics<\/jats:italic> 15, 958\u2013975) We check the finite sample performance via Monte Carlo simulations and empirical applications.<\/jats:p>","DOI":"10.1017\/s0266466621000402","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T09:58:12Z","timestamp":1628848692000},"page":"146-188","source":"Crossref","is-referenced-by-count":7,"title":["COMPLETE SUBSET AVERAGING FOR QUANTILE REGRESSIONS"],"prefix":"10.1017","volume":"39","author":[{"given":"Ji 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