GrFA (version 0.2.2)

CCA: Canonical Correlation Estimation

Description

Canonical Correlation Estimation for Group Factor Model.

Usage

CCA(
  y,
  rmax = 8,
  r0 = NULL,
  r = NULL,
  localfactor = FALSE,
  method = "CCD",
  type = "IC3"
)

Value

An object of class "GFA" containing:

r0hat

The estimated number of global factors.

rhat

The estimated number of local factors (if localfactor = TRUE).

rho

The vector of average canonical correlations (eigenvalues).

Ghat

The estimated global factors.

Fhat

The estimated local factors (if localfactor = TRUE).

loading_G

A list consisting of the estimated global factor loadings.

loading_F

A list consisting of the estimated local factor loadings (if localfactor = TRUE).

residual

A list consisting of the residuals (if localfactor = TRUE).

threshold

The threshold used in determining the number of global factors (only for method = "MCC").

Arguments

y

A list of the observation data, each element is a data matrix of each group with dimension \(T \times N_m\).

rmax

The maximum factor numbers of all groups. Default is 8.

r0

The number of global factors. Default is NULL, the algorithm will automatically estimate the number of global factors. If you have prior information about the true number of global factors, you can set it manually.

r

The number of local factors in each group. Default is NULL, the algorithm will automatically estimate the number of local factors. If you have prior information, set it manually as an integer vector of length \(M\) (the number of groups).

localfactor

Logical. If FALSE (default), local factors are not estimated. If TRUE, local factors will be estimated.

method

The method used in the algorithm. Default is "CCD", can also be "MCC".

type

The method used in estimating the factor numbers in each group initially. Default is "IC3".

References

Choi, I., Lin, R., & Shin, Y. (2021). Canonical correlation-based model selection for the multilevel factors. Journal of Econometrics.

Examples

dat <- GrFA::gendata()
CCA(dat$y, rmax = 8, localfactor = TRUE, method = "CCD")
CCA(dat$y, rmax = 8, localfactor = TRUE, method = "MCC")