infocrit: Selection criteria for the approximate factor model

In GCCfactor: GCC Estimation of the Multilevel Factor Model

Selection criteria for the approximate factor model

Description

This function performs model selection for the (2D) approximate factor model and returns the estimated number of factors.

Usage

infocrit(Y, method, r_max = 10)

Arguments

Y	A T \times N data matrix. T = number of time series observations, N = cross-sectional dimension.
method	A character string indicating which criteria to use.
r_max	An integer indicating the maximum number of factors allowed. 10 by default.

Details

"method" can be one of the following: "ICp2" and "BIC3" by Bai and Ng (2002), "ER" by Ahn and Horenstein (2013), "ED" by Onatski (2010).

Value

The estimated number of factors.

References

Bai, J. and Ng, S., 2002. Determining the number of factors in approximate factor models. Econometrica, 70(1), pp.191-221.

Ahn, S.C. and Horenstein, A.R., 2013. Eigenvalue ratio test for the number of factors. Econometrica, 81(3), pp.1203-1227.

Onatski, A., 2010. Determining the number of factors from empirical distribution of eigenvalues. The Review of Economics and Statistics, 92(4), pp.1004-1016.

Examples

# simulate data

T <- 100
N <- 50
r <- 2
F <- matrix(stats::rnorm(T * r, 0, 1), nrow = T)
Lambda <- matrix(stats::rnorm(N * r, 0, 1), nrow = N)
err <- matrix(stats::rnorm(T * N, 0, 1), nrow = T)
Y <- F %*% t(Lambda) + err

# estimation

r_hat <- infocrit(Y, "BIC3", r_max = 10)

GCCfactor documentation built on Nov. 2, 2023, 5:59 p.m.