rra_est: Reduced-Rank Approach (RRA) factor extraction

In sdim: An R Package for Supervised Dimension Reduction

Reduced-Rank Approach (RRA) factor extraction

Description

Implements the two-step GMM estimator of He, Huang, Li, and Zhou (2023). Factor proxies X are rotated to maximise explanatory power for the target return matrix target, using diagonal GMM weighting matrices.

Usage

rra_est(target, X, nfac, compute_stat = FALSE)

Arguments

target	Numeric matrix (T x N) of target variables (e.g., asset returns). A vector is coerced to a T x 1 matrix.
X	Numeric matrix or data frame (T x L) of factor proxies.
nfac	Positive integer; number of RRA factors to extract.
compute_stat	Logical; if TRUE, compute the GMM J-test statistic for overidentifying restrictions. Returned as NULL when FALSE (default) or when degrees of freedom <= 0.

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)
Y <- matrix(rnorm(100 * 5), 100, 5)
fit <- rra_est(target = Y, X = X, nfac = 3)
print(fit)

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