BAYESIAN DYNAMIC VARIABLE SELECTION IN HIGH DIMENSIONS
针对时变参数回归模型中存在大量预测变量的情况，提出一种新的动态变量选择方法，能判断每个时期哪些预测变量对预测目标有用，并用400多个宏观、金融和全球变量预测通胀，表现优异。
Abstract This article addresses the issue of inference in time‐varying parameter regression models in the presence of many predictors and develops a novel dynamic variable selection strategy. The proposed variational Bayes dynamic variable selection algorithm allows for assessing at each time period in the sample which predictors are relevant (or not) for forecasting the dependent variable. The algorithm is used to forecast inflation using over 400 macroeconomic, financial, and global predictors, many of which are potentially irrelevant or short‐lived. The new methodology is able to ensure parsimonious solutions to this high‐dimensional estimation problem, which translate into excellent forecast performance.