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COMPLETE SUBSET AVERAGING FOR QUANTILE REGRESSIONS
Econometric Theory ( IF 0.8 ) Pub Date : 2021-08-13 , DOI: 10.1017/s0266466621000402
Ji Hyung Lee 1 , Youngki Shin 2
Affiliation  

We propose a novel conditional quantile prediction method based on 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, Journal of Econometrics 188, 40–58), we investigate the large sample properties of CSA and show the asymptotic optimality in the sense of Li (1987, Annals of Statistics 15, 958–975) We check the finite sample performance via Monte Carlo simulations and empirical applications.



中文翻译:

分位数回归的完整子集平均

我们针对分位数回归提出了一种基于完整子集平均 (CSA) 的新型条件分位数预测方法。考虑中的所有模型都可能被错误指定,并且随着样本量的增加,回归变量的维度趋于无穷大。由于我们对完整的子集进行平均,模型的数量远大于采用复杂加权方案的通常模型平均方法。我们建议使用相等的权重,但基于留一法交叉验证方法选择完整子集的适当大小。基于 Lu 和 Su (2015, Journal of Econometrics 188, 40–58)的理论,我们研究了 CSA 的大样本特性,并展示了 Li (1987, Annals of Statistics)意义上的渐近最优性15, 958–975) 我们通过蒙特卡罗模拟和经验应用检查有限样本性能。

更新日期:2021-08-13
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