Identifying relevant and irrelevant variables in sparse factor models
在贝叶斯框架下用稀疏先验估计因子模型，提出识别相关与无关变量的方法，模拟和实证表明能准确区分两类变量，对处理多国GDP和美国通胀等大数据集有用。
Summary This paper considers factor estimation from heterogeneous data, where some of the variables—the relevant ones—are informative for estimating the factors, and others—the irrelevant ones—are not. We estimate the factor model within a Bayesian framework, specifying a sparse prior distribution for the factor loadings. Based on identified posterior factor loading estimates, we provide alternative methods to identify relevant and irrelevant variables. Simulations show that both types of variables are identified quite accurately. Empirical estimates for a large multi‐country GDP dataset and a disaggregated inflation dataset for the USA show that a considerable share of variables is irrelevant for factor estimation.