RTS {HDRFA}

Robust Two Step Algorithm for Large-Dimensional Elliptical Factor Models

Description

This function is to fit the large-dimensional elliptical factor models via the Robust Two Step (RTS) algorithm.

Usage

RTS(X, r)

Arguments

X

Input matrix, of dimension T\times N. Each row is an observation with N features at time point t.

r

A positive integer indicating the factor numbers.

Details

See He et al. (2022) for details.

Value

The return value is a list. In this list, it contains the following:

Fhat

The estimated factor matrix of dimension T\times r.

Lhat

The estimated loading matrix of dimension N\times r.

Author(s)

Yong He, Lingxiao Li, Dong Liu, Wenxin Zhou.

References

He, Y., Kong, X., Yu, L., Zhang, X., 2022. Large-dimensional factor analysis without moment constraints. Journal of Business & Economic Statistics 40, 302–312.

Examples

set.seed(1)
T=50;N=50;r=3
L=matrix(rnorm(N*r,0,1),N,r);F=matrix(rnorm(T*r,0,1),T,r)
E=matrix(rnorm(T*N,0,1),T,N)
X=F%*%t(L)+E

fit=RTS(X,3)
fit$Fhat;fit$Lhat