heyongstat / rts

Robust Two Step (RTS) Method for Large-dimensional Elliptical Factor Model

Large-dimensional factor model has drawn much attention in the big-data era, in order to reduce the dimensionality and extract underlying features using a few latent common factors. Conventional methods for estimating the factor model typically requires finite fourth moment of the data, which ignores the effect of heavy-tailedness and thus may result in unrobust or even inconsistent estimation of the factor space and common components. In this paper, we propose to recover the factor space by performing principal component analysis to the spatial Kendall's tau matrix instead of the sample covariance matrix. In a second step, we estimate the factor scores by the ordinary least square (OLS) regression. Theoretically, we show that under the elliptical distribution framework the factor loadings and scores as well as the common components can be estimated consistently without any moment constraint. Reference:

He Y., Kong X., Yu L. and Zhang X. (2019). Large-dimensional Factor Analysis without Moment Constraints, arXiv:1908.04957

Please see https://arxiv.org/abs/1908.04957