Selection inconsistency for factor-augmented regressions

Abstract

Listen

Translate article

Factor-augmented regression (FAR) is an effective tool in forming predictions in the presence of big data set. However, few studies have considered the selection of latent factors and observed covariates simultaneously in FAR. We discover that the class of information criteria suggested by Groen and Kapetanios (2013) to determine the factors and covariates fail to provide consistent model selection, especially when regressors are correlated and the cross-section dimension is small relative to the sample size. This theoretical discovery is corroborated with simulation findings that the criteria in Groen and Kapetanios (2013) tend to underestimate the factor number but overestimate the covariate number.