统计与决策2024，Vol.40Issue(12)：35-41,7.DOI:10.13546/j.cnki.tjyjc.2024.12.006
分位数因子增广的分位数自回归分布滞后模型构建
Construction of Quantile Factor-augmented Quantile Autoregression Distributed Lag Model
摘要
Abstract
Factor-augmented regression is an important method to forecast macroeconomics using high dimensional data.However,the results of factor models and regression models under the mean regression framework are not robust enough in the face of outliers or thick tail distributions.In view of this,the paper constructs a quantile factor-augmented quantile autoregression distributed lag model under quantile regression frame.In this model,the quantile factor model is constructed to reduce the dimen-sionality of high-dimensional data,with the common factors of different quantiles extracted.Further,the quantile autoregressive distributed lag model is constructed by using the extracted common factors and lag terms of response variables.Numerical simula-tion results show that the mean and non-mean factors estimated by quantile factor analysis are more robust in the case of data out-liers or thick tail distribution.The predictive performance of quantile factor-augmented regression is better than that of fac-tor-augmented regression,and the predictive performance of quantile factor-augmented autoregressive distributed lag model is better than that of the benchmark model.关键词
分位数因子/分位数回归/因子增广回归/自回归分布滞后模型Key words
quantile factor/quantile regression/factor-augmented regression/autoregressive distributed lag model分类
管理科学引用本文复制引用
黄玉婷,傅德印..分位数因子增广的分位数自回归分布滞后模型构建[J].统计与决策,2024,40(12):35-41,7.基金项目
甘肃省优秀博士生项目(23JRRA1189) （23JRRA1189）
甘肃省研究生"创新之星"项目(2023CXZX-700) （2023CXZX-700）
兰州财经大学博士研究生科研创新项目(2022D02 （）
2022D05) （）
