pca_est: PCA factor extraction

In sdim: An R Package for Supervised Dimension Reduction

PCA factor extraction

Description

PCA factor extraction

Usage

pca_est(target = NULL, X, nfac, gamma = -1)

Arguments

target	Ignored; accepted for API uniformity with other estimators.
X	Numeric matrix or data frame (T x L) of factor proxies.
nfac	Positive integer; number of factors to extract.
gamma	Numeric scalar controlling mean adjustment in the second-moment matrix. 'gamma = -1' (default) gives the sample covariance (traditional PCA). 'gamma = 10' and 'gamma = 1' give the Lettau-Ludvigson variants from He et al. (2023).

Value

An object of class "sdim_fit".

References

He, J., Huang, J., Li, F., and Zhou, G. (2023). Shrinking Factor Dimension: A Reduced-Rank Approach. Management Science, 69(9). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1287/mnsc.2022.4563")}

Examples

set.seed(1)
X <- matrix(rnorm(100 * 8), 100, 8)
fit <- pca_est(X = X, nfac = 3)
print(fit)

sdim documentation built on July 15, 2026, 1:10 a.m.