PCA_FN: Estimating Factor Numbers via Eigenvalue Ratios Corresponding...

In HDRFA: High-Dimensional Robust Factor Analysis

Estimating Factor Numbers via Eigenvalue Ratios Corresponding to PCA

Description

This function is to estimate factor numbers via eigenvalue ratios corresponding to Principal Component Analysis (PCA).

Usage

PCA_FN(X, rmax)

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.

Details

See Ahn and Horenstein (2013) for details.

Value

rhat	The estimated factor numbers.

Author(s)

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

References

Ahn, S.C., Horenstein, A.R., 2013. Eigenvalue ratio test for the number of factors. Econometrica 81, 1203–1227.

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

PCA_FN(X,8)

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