Factor-Adjusted Model Averaging
因子调整的模型平均
We propose a model averaging method for high-dimensional regression with highly correlated covariates. We use a factor structure to model the covariate dependence, allowing the covariates to be decomposed into two uncorrelated or weakly correlated latent components: common factors and idiosyncratic components. The number of common factors is allowed to diverge. We average estimators from factor-adjusted candidate models with augmented predictors composed of estimated common…