吉林大学学报（理学版）2025，Vol.63Issue(5)：1313-1324,12.DOI:10.13413/j.cnki.jdxblxb.2024478
混频数据分位回归模型的Bayes分析
Bayesian Analysis of Quantile Regression Model for Mixed Frequency Data
摘要
Abstract
Aiming at the problem of modeling mixed frequency data,we proposed an autoregressive U-MIDAS(unrestricted mixed data sampling)quantile regression model.Firstly,we combined the nested Lasso penalty method and the spike-and-slab prior for Bayesian parameter estimation and variable selection.Secondly,the superiority of this method was proved by numerical simulations.Finally,this method was applied to predict the annualized quarterly growth rate of nominal gross domestic product(GDP)in the United States.The results show that the proposed method has good prediction accuracy.关键词
混频数据/自回归U-MIDAS分位回归模型/Bayes分析/嵌套Lasso惩罚Key words
mixed frequency data/autoregressive U-MIDAS quantile regression model/Bayesian analysis/nested Lasso penalty分类
数理科学引用本文复制引用
董小刚,叶盼盼,袁晓惠,孙长智..混频数据分位回归模型的Bayes分析[J].吉林大学学报（理学版）,2025,63(5):1313-1324,12.基金项目
国家社会科学基金(批准号:23BTJ047)、国家自然科学基金(批准号:12271060)和国家自然科学基金天元基金(批准号:12226416). （批准号:23BTJ047）
