IQR_FN: Estimating Factor Numbers via Rank Minimization Corresponding...

In HDRFA: High-Dimensional Robust Factor Analysis

Estimating Factor Numbers via Rank Minimization Corresponding to IQR

Description

This function is to estimate factor numbers via rank minimization corresponding to Iterative Quantile Regression (IQR).

Usage

IQR_FN(X, rmax, tau, threshold = NULL, L_init = NULL, F_init = NULL, 
       max_iter = 100, eps = 10^(-6))

Arguments

X	Input matrix, of dimension T\times N. Each row is an observation with N features at time point t.
rmax	The user-supplied maximum factor numbers.
tau	The user-supplied quantile level.
threshold	The threshold of rank minimization; default is NULL.
L_init	User-supplied inital value of loadings in the IQR; default is the PCA estimator.
F_init	User-supplied inital value of factors in the IQR; default is the PCA estimator.
max_iter	The maximum number of iterations. The default is 100.
eps	The stopping critetion parameter of the IQR method. The default is 1e-06.

Details

See Chen et al. (2021) for more details.

Value

rhat	The estimated factor number.

Author(s)

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

References

Chen, L., Dolado, J.J., Gonzalo, J., 2021. Quantile factor models. Econometrica 89, 875–910.

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

tau=0.5
IQR_FN(X,8,tau)

HDRFA documentation built on Sept. 11, 2024, 9:25 p.m.